Melina Petsolari
Abstract
How might emerging modalities (e.g., NLP) be leveraged to transform the provision of parenting support? To explore the role of AI technologies in supporting parenting behaviour—and child-well-being—we surveyed 92 parents to gather their perspectives on nine future-oriented scenarios. We used Design Fiction and Speed Dating to understand parents needs and preferences around the design of agent-based supports. We explore the perceived benefits of AI assistants (i.e., receiving objective feedback, managing emotions and personalised guidance) and the most voiced concerns (i.e., AI undermining parental authority, replacing human interactions, and promoting lazy parenting). Finally, we highlight a number of plausible design directions based on the scenarios that parents were positive about.
1 INTRODUCTION
Children’s well-being is greatly influenced by their family environment [304]. Parenting behaviour is one of the most modifiable ways of supporting children’s well-being [126, 200, 215] compared with other risk factors (e.g., poverty, single-parent household and parent depression) [67, 84, 111, 113, 315]. In light of this, various parenting programs have been developed to support and guide parents in cultivating positive parenting practices and improving family well-being [296]. However, despite their availability and promising outcomes [39, 102], parent training (PT) programs often fall short due to low-engagement, lack of personalisation and limited contextual understanding [28, 72, 176, 302, 323]. Human-Computer Interaction (HCI) research has made important contributions towards designing for supporting well-being, family dynamics and parent-child interactions. For example, prior work has considered designing for in-the-moment support, increasing awareness of parent roles, and fostering empathy and reflection (e.g., [146, 270, 326, 366]) yet, only limited work has investigated how new techniques (e.g., Artificial Intelligence (AI), Natural Language Processing (NLP)) could transform the provision of parenting support, skill development and in turn, behaviour change. In this paper, we investigate the opportunities and tensions for designing digital supports with new modalities/techniques (such as those found in agent-based systems) in this sensitive space.
With the increasing presence of AI-mediated technologies across domestic life, AI technologies could potentially support parenting by, for example, supporting knowledge acquisition through access to timely and relevant parenting support [197], increasing productivity by automating certain tasks [337], or by quantifying parent and child technology habits, AI literacies and learning needs [109]. However, with AI becoming more and more embedded in domestic family life, it is important to critically consider how evolving AI technology applications can impact on parenting, both positively and negatively. Given the speed that AI powered systems are being utilised across diverse application areas rather than cautiously introducing and implementing through design [6], it is not always clear what decision making processes inform what is prioritised. Little research has considered how different AI functionalities directly relate to evidence-based parenting approaches, whilst also considering the parent perspective of how AI-mediated parenting support could impact on their daily lives. This points to the opportunity to lay the groundwork for understanding if and how we can employ AI and NLP techniques to begin to address parenting needs. While keeping in mind that any potential AI-mediated system may still have considerable technological limitations, in this project we were specifically interested in examining if there was an opportunity for:
(1) | AI technology/conversational agents to be utilised for parent training and support; and | ||||
(2) | designers to explore what these applications might look like so that their design could potentially enable behaviour change through situated support. | ||||
We begin this process by synthesising what is already known in the parenting literature—grounded in psychological theories—to create fictional accounts of future technologies that might support parenting. Through design fiction [50, 218], we aimed to better understand how we can situate AI based systems in the parenting domain. Similar approaches have been utilised by HCI researchers in educational, developmental, and health domains (e.g., [54, 60, 62, 123, 277, 290, 309, 340, 349, 367]), suggesting that fictions can take a key role in envisaging how HCI could engage and design these technologies (e.g., [3, 48, 116, 143, 281, 286, 314, 350]). To foster conversation about supporting future parent-centred AI-mediated interactions, we conducted a mixed method study where we explored how AI techniques (e.g., NLP) might be drawn on to create new applications that support parenting (i.e., optimizing delivery of intervention components in accordance to user needs and acceptance).
To this end, we sought to answer the following research questions:
RQ.1 | What are some of the plausible functionalities that an agent-based system could potentially possess to effectively meet parenting needs and provide in-situ support and learning? | ||||
RQ.2 | How do parents react to design fictions of AI assistants for parenting support? | ||||
RQ.3 | What do parents perceive as potential benefits and frictions exist in this emerging design space? | ||||
To answer the research questions, we present a three-step study where we created future-oriented design fictions and explored the possibilities and implications of these emerging AI technologies in the context of parenting support. As a first step, to theoretically ground our fictional scenarios, we drew on prior empirical and theoretical work and attempted to establish a framework for understanding the specific needs of parents and how technology could support them (RQ1). We then iterated this framework through interviews with experts (in the psychology and parenting domain). Second, using this framework we designed nine fictional scenarios illustrating parenting needs and the potential effectiveness of agents/NLP techniques in addressing them (RQ1). Finally, we used the speed dating with storyboards/scenarios method [99, 370] and operationalized our scenarios through survey with 92 parents who reacted and provided feedback toward the technologies we envisioned (RQ2 and RQ3).
Our survey findings showed that parents expressed mixed reactions to the potential benefits of AI in the presented parenting scenarios, as well as skepticism and concern about how specific AI functionalities would impact on genuine parent-child interactions and family dynamics. Our paper contributes:
i.) | an expert-informed and evidence-based conceptual framework for identifying what parenting support scenarios should incorporate, and | ||||
ii.) | empirical research on parent reactions to the envisaged opportunities of AI-mediated technology. | ||||
2 BACKGROUND AND RELATED WORK
2.1 Parenting Education and Technology
Parent training (PT) is a widely recognized as an effective evidence-based treatment for addressing disruptive behaviors in children. Through PT, parents are equipped with the knowledge, skills, and strategies (e.g., praise, time-out) to effectively manage their child’s challenging behaviors [247, 354]. This form of training empowers parents to understand the underlying causes of their child’s behavior and implement appropriate strategies that promote positive change. It has consistently shown positive results in improving both child and parent outcomes, including enhanced child behavior; reduced parental stress; and improved overall family functioning [293, 322]. PT is typically delivered in-person, through manualised curricula (e.g., theory, practical examples) and involves activities like role plays, discussions, video demonstrations, and practice of parent-child interaction techniques during sessions or as homework [20, 182]. Although these interventions have positive effects on parent and child outcomes, in-person programs are resource-intensive and face challenges in reaching, engaging, and retaining parents [12, 112, 130, 235]. In addition, parents—who do make it to the sessions—report difficulties in applying the learned techniques at home [243]. Varying reasons contribute to lack of skill generalisability [86, 334] such as difficulty in changing established habits; lack of cooperation from their partners in implementing the techniques; allocating sufficient time for joint parenting; and incorporating the techniques into their already demanding lives [243]. Ultimately, a program’s effectiveness depends on parents consistently applying the acquired skills in the appropriate settings and with the intended targets.
Digital parenting interventions have emerged as a solution to some of these challenges [37, 135, 235, 282]. These interventions have been adapted from an existing evidence-based in-person parent training program (e.g., [301]). They vary in delivery, with most focusing on structured teaching methods and skill development (i.e., requiring parents to progress through modules in a sequential pathway) [13, 22, 87, 225], while others focus on psychoeducation (e.g., [11, 24, 256]). In addition, they use a variation of technology components (e.g., audio, video, text, quizzes, skills training or coaching) to deliver the program content [225]; and interactive features (e.g., discussion boards or gamified content) to engage parents, promote self-efficacy, and encourage problem-solving [13, 24, 87, 249, 256]. Despite their promise [39, 102], digital PT programs often fall short in engaging parents as they require significant levels of motivation and commitment to finish the program [57] which can be very taxing on parental time and effort. In addition, lack of accountability (e.g., not having peer support to discuss and motivate, nor having milestones to achieve), limited access to proper technological supports (e.g., not having broad access to the internet and/or computers/phones for individual use) and limited contextual understanding and personalisation (i.e., not directly providing support given the particular dynamics within a family) further contribute to parents low engagement rates [28, 103, 176, 302, 323, 365]. As such, the acquisition and practise of skills is hard to obtain, implement and maintain. While adapting existing interventions into programs delivered online (instead of in-person) might increase scalability and reach (e.g., [25, 58]) there remains a need to design for situated support and learning (i.e., effectively scaffold the implementation of skills to their everyday life) to support long-term outcomes [103, 108, 152, 305].
2.2 Emerging Technologies in Parenting
A growing body of work suggests that technology-enabled supports can effectively scaffold parent-child interaction, well-being and family dynamics (e.g., [124, 146, 188]). Yet, limited work has explored how new modalities (i.e., NLP) might be leveraged –for situated-support and learning– in this sensitive space. The following, provide an overview of the directions HCI research has taken in supporting positive family functioning.
Family Informatics.
HCI research on personal informatics (PI) has shifted from individual data collection to studying collective and situated experiences – in particular within the realm of family systems [270]. Family informatics research has investigated a myriad of ways in supporting family activities and needs, such as supporting communication of daily activities in the family via a shared message board [262], coordinating family routines through (digital) calendars[100], sharing of health information among inter-generational family members [45], supporting healthy eating practices [88] and exchanging family fitness activity data using mobile apps (e.g., Storywell [291]). Other work has explored how tracking for children has implications within the broader family ecosystem. For instance, Wang et al. [351] and Lupton et al. [222] have demonstrated that implementing PI tools to track children’s data not only improves parents’ understanding of their child’s patterns but also reduces the need for frequent physical check-ups by facilitating the transfer of baby-related information to mobile applications. Nonetheless, by using tracking tools to monitor baby and child habits, these ubitiquitous technologies can increase parents’ anxiety, highlighting the importance of considering their emotional well-being and input when designing parenting supports [351].
Real-Time Parenting Supports.
While the aforementioned examples centered on situated experiences among family members, other work has specifically investigated the integration of active ingredients (i.e., theory-based change methods such as teaching reflection, empathetic listening and emotion-regulation [41, 356]) into the intervention using diverse delivery methods [1, 168, 182, 185, 210, 212, 213]. For example, Kim et al. [188] utilised reflective practice to support parent-child interactions via a wearable smart mirror designed to quickly give parents their child’s view during their interactions (second person view). Giannakos et al. [146] also used reflection to encourage parental awareness of the different roles they can take during collaborative activities with their child through a tangible device (Awareness Object). Separately, Pina et al. [268], investigated just-in-time stress coping interventions for parents with children with Attention Deficit Hyper-Activity Disorder (ADHD). They found that providing prompts right before a full escalation of stress occurs was highly effective, as parents were more receptive to intervention strategies during that critical period. Finally, WAKEY [74] teaches parents communication strategies for conflict resolution during morning routines.
Researchers have also investigated the use of real-time interventions to assist parents during their interactions with children who are impacted by language disorders. For instance, TalkBetter [141] aimed to support parents communication skills through the monitoring of conversational turns – providing personalized feedback on communication strategies. Similarly, TalkLime [329] monitors turns while using real-time visualization on the parent’s phone screen to induce behavior change. Lastly, SpecialTime [171] uses automatic evaluation of parent language during Parent-Child Interaction Therapy (PCIT) and provides real-time feedback on spoken dialogue acts between parent and child in-situ, removing the barrier for clinical visits. These findings suggest that feedback on language use is important in various parenting settings thus, we explore the opportunities and limitations of designing language-delivered supports in various parenting and learning contexts.
Augmenting Parenting Through Conversational Agents.
Language-based technologies, often called "conversational AI", "dialogue systems", "chat-bots", "virtual assistants" or "conversational agents" (i.e., both text-based and speech- or voice-based modalities for input and output) [128, 175, 221, 273, 279], have increasingly been adopted by parents and children alike. AI assistants that use Automatic Speech Recognition (ASR) [38] and Natural Language Understanding (NLU) [151, 359] add a new dimension to how home technology can enhance parent-child interaction and influence parenting behaviors. Whilst conversational agents often have a separate goal (e.g., information retrieval), research has shown that they also impact on family dynamics. For instance, research has recognized potential areas for utilizing conversational assistants to support the educational, developmental, and social well-being of children (e.g., [35, 110, 209, 226, 357, 364]). In addition, Beneteau et al. [35] investigated parents and children’s interactions with Amazon Alexa in-situ. Through a 4-week deployment they found that voice interfaces enhance verbal communication and improve children’s communication skills. Separately, Storer et al. [335] found that smart speakers can support more accessible parent-child interactions for mixed-visual-ability families.
Another line of research has explored the use of Artificial Companions (AC) or robots to support wide-ranging child-rearing activities. For instance, Fink et al. [133] showed that long-term interactions with robots can motivate children to tidy their room; as well as motivate children’s reading habits and support their reading comprehension [239]. Separately, Emobie [8] teaches children social skills and coping strategies when dealing with negative emotions (e.g., guiding them through breathing exercises).
ACs have been also designed to support the delivery of autism interventions [303] and educational interventions [31] Finally, ACs have also been assigned the role of a teacher, trainer, tutor, or coach [70, 71, 76, 77, 150, 244, 368] and demonstrate educational competencies by guiding users through activities, teaching new skills, whilst providing personalized learning and tailored support [29, 285]. To this end, ACs have proven effective in enhancing learner motivation and performance (e.g., [36, 77, 83, 285, 330]).
While these studies provide important insights into how adult carers and children perceive, interact, and learn with AI assistants, they do not address the possible roles these agents can support within the family ecosystem and in particular in the context of parent training. As such, examining parents’ needs and preferences regarding the agents’ role in supporting family functioning can provide valuable insights for designing effective AI-driven parenting supports. In the following section,we discuss the ways in which AI supports can support skill development, scale up and improve the delivery of behaviour change interventions and mental health treatment.
2.3 AI Technologies for Mental Well-being
The development of conversational AI systems that can interact coherently and engagingly with users has been an active area of research within HCI, NLP and Machine Learning (ML). A growing body of literature in HCI and health has designed various technologies (e.g., self tracking, conversational agents, tangible interfaces) to promote mental health and well-being [92, 292, 328]. For instance, several self-tracking tools have been developed to self-monitor people’s stress [81], mood [165, 205] and sleep [79] which can help increase self-awareness and motivate health-related behavior change [186, 278, 280]. In addition, conversational agents (CA) have proven useful in helping people monitor their feelings and behaviors [191], encourage deep self-disclosure in journaling practice [207, 209], and improve well-being [134, 208]. Previous studies suggest that CAs can serve as virtual companions that track behaviors, listen without judgment and offer proactive guidance (e.g., cognitive-behavioral therapy) to improve mental well-being through conversational interactions [134, 307]. Further, CAs can coach users in developing different skills (e.g., [352]) as well as improve their mental well-being by training their thoughts and behavior (e.g., [219, 363]). For instance, Wang et al. [352] developed a CA that coaches people to relieve their public-speaking anxiety via cognitive reconstruction exercises; while Gabrielli et al. [142] proposed a chatbot-based coaching intervention that successfully helped adolescents learn life skills (e.g., strategies for coping with bullying).
Despite ongoing efforts to develop AI for mental health support (as per above), the implementation of the AI vision is a complex endeavor. Specifically, most research often focuses on technical feasibility with less consideration of real-world clinical impact [2, 78, 129, 333]. As such, a substantial gap exists between what has been demonstrated in research settings and what has been validated in clinical practice [107, 274, 276]. In addition, many AI models in healthcare go unused due to the lack of understanding on how and for whom to use them effectively [73, 284]. This leads to a disconnect between AI development and user needs, thereby reducing its effectiveness and adoption. Lastly, identifying the active ingredients of an intervention (i.e., specific components that serve as key drivers of change) is crucial for unpacking the intervention black box to pinpoint the ways in which AI can amplify their use. Therefore, integrating AI into an intervention, should not be simply about deploying a ML model but rather viewing it as an enabling component of a broader solution [217].
2.4 Design Fiction and World-building
Our design research exploration draws on various design futuring paradigms of critical design [17, 18], speculative design [114, 125], and design fiction [51, 59, 65, 169, 361]. Critical design aims to challenge assumptions by enabling users to evaluate concepts and uncover hidden dimensions such as values, ethics, personal beliefs (or disbelief’s), and identify potential flaws [117, 160, 361, 362]. The objective of critical design is to involve users in a constructive debate and envision alternative perspectives [18]. Another approach in exploring future possibilities is through speculative design, which focuses on the use of creative/generative thinking to construct sociological, technical, and unconventional ideas (among others). In this way, speculative design can “make a whole range of viable and not so viable possibilities tangible and available for consideration” [114]. Building on these design approaches, we used design fiction to construct AI assisted parenting futures where our design concepts lived [19, 90]. Design fiction is a useful methodology that enables the envisioning of novel designs and technologies through world building while positioning them within a fictional context. Design fiction is not pure imagination but instead, it (typically) entails conceiving of a relatively plausible new technology based on current trends and encourages users to critically examine potential use cases and consequences [48, 161, 258, 267].
2.5 Summary and Motivation
A number of opinion pieces suggest that Human-AI collaboration might play a role in future parenting interventions ([109, 368] as per above). However, it is not yet clear what the interaction design patterns would be for these systems: we do not yet have models of where and how AI-assisted interactions would support parenting. One approach to better understand how we can situate AI within the parenting domain—accepting the high development cost for any specific feature—is to rely on design fiction methodologies, as per prior work in other domains. Our approach shares similarities with prior HCI research that has argued for exploring the potential roles, values, and implications of emerging technologies by using multiple design proposals to establish a better overall understanding of the design space (e.g., [42, 361]).
In what follows, we start by developing a conceptual framework to synthesise the core under-supported parenting needs (as known in the parenting literature) with a set of plausible roles that AI-driven technology could play (drawing on prior examples and suggested opinions in the literature). Second, we use this framework to develop nine scenarios depicting parenting needs and the potential effectiveness of agents/NLP techniques in addressing them. Finally, we draw on these scenarios to gather perspectives from parents (n=92) through survey to better understand which of the futures envisioned (if any) might be desirable (see Figure 1 for an outline of our process).
Figure 1: Overview of the process we followed, structured in three steps, and the resulting outcomes of each step.
3 METHODOLOGY
3.1 Phase 1. Conceptual Framework Development
To understand how we might design for this emerging space we first need a rigorous, evidence-based understanding of what are the (parenting) challenges and intervention mechanisms that should be supported. To do this we developed a framework (drawing on prior empirical and theoretical work) in an attempt to identify the specific needs of parents and how technology could support them. We then explored the potential synergy between the design targets—role of technology—in supporting these challenges. This process enabled us to design nine scenarios that were theoretically grounded and relevant, based on the parenting literature.
We developed the conceptual framework in 3 phases: As a first step, we conducted a literature search on parenting needs (e.g., [63, 157, 183, 196, 235, 240, 241, 324]), the learning and behavioural sciences (e.g., [14, 15, 21, 144, 147, 224, 230]) and intelligent system taxonomies/delivery approaches (e.g., [57, 87, 115, 120, 189, 190, 245, 278]). Rather than carrying out an exhaustive literature analysis across these fields, we sought to draw research-based inspiration for our scenarios [48]. The literature survey enabled us to discern specific parenting needs and their relationship to digital parenting support. Based on these findings, we compiled a list of needs that seemed important enough to assess, as well as a catalog of technology approaches and delivery mechanisms that could potentially support them. To further refine our list, we identified gaps in existing digital parenting support literature and opportunities for technology to address parents’ needs effectively. This process enabled us to identify six initial categories of parenting needs and technologies that might support them. Finally, we iterated and validated the categories and descriptors we developed by holding interviews with three psychology experts (two early childhood psychology researchers and one principal clinical psychologist from the UK National Health Service (NHS)). The expert feedback resulted in modifications to the content and structure of the conceptual framework, resulting in the inclusion of three additional parenting dimensions. In terms of the technology dimensions the experts provided insights on the potential implications of these modalities however, without any additions or removals.
Figure 2: Snapshot of process: a. Populated Framework, b. Combinations of cells for scenario building, c. Overview of nine futures. See supplementary documents for higher-resolution images.
3.2 Phase 2. Scenario Development
3.2.1 Populated matrix: 2-dimensional design space.
During this phase we used the conceptual framework consisting of a set of (a) parenting theories from psychology literature and (b) technology mechanisms to develop our scenarios. The resulting 2-dimensional design space, consisting of parenting needs on one axis, can be applied to human-centered technologies/mechanisms on the other axis (see Figures 2 a and 2 b for a snapshot of these opportunities which we describe in full in Table 1 & 2 and Appendix A). Specifically, we created a matrix and populated it by identifying the connections between parenting needs and the role of technology in supporting them. This generative approach enabled us to outline various combinations of potential targets (parenting needs) and the corresponding interaction techniques (delivery mechanisms). As a result, we generated 54 potential combinations of needs and mechanisms (see Figure 2 a) to consider for inclusion in our scenarios. To determine which cell combinations we should focus and build our scenarios on, we generated the following three levels/questions:
• | Applications (Possibilities): How might AI assisted technologies support parents needs? | ||||
• | Implications (Probabilities): How might a combination of needs and learning mechanisms (technologies) support parenting? | ||||
• | Generative (Ideation): How might we design parent-agent interaction for in-situ support and learning? | ||||
These levels/questions guided our decision-making process in determining the cells we should prioritise. By following this approach, we identified the combinations (see Figure 2 b) that guided the design of our nine, fictional, theoretically grounded scenarios (see Figure 2 c). In the rest of the paper, we detail each dimension of the framework and the combinations that resulted in the final nine scenarios (cf., 4.1).
3.3 Phase 3. Survey Design
3.3.1 Speed Dating.
We used the Speed-dating [99] with storyboards method to gather parents reactions. Speed dating allows the presentation of information in a snapshot; is helpful in directing participants’ focus towards particular instances of technological behaviors/interactions and enables ease of data collection [370]. To elicit participant reactions and scope our scenarios, we created a Qualtrics-based survey consisting of three parts, taking approximately 15-20 minutes to complete. After consenting to participate in the study, participants were presented with the scenarios in random order. In the first part of the survey, participants were introduced to the three storyboards of each scenario and asked to reflect on the scenario they saw and evaluate whether they would like to reside in the envisioned future. We first, asked participants to: "Consider the scenario you just saw: Would you like to live in this future?" on a five-point Likert item question from "Definitely" to "Definitely Not" (Q1). Once they selected an answer for this question, we prompted them to "Please briefly explain your choice" through an open text field. The second question asked participants the likely-hood of using the technology depicted in the scenario, "How likely or unlikely would you be to use this app to help in your parenting?" on a seven-point Likert item question from "Extremely unlikely" to "Extremely likely" (Q2). The third question gauged the ease or difficulty of understanding the scenario in question, "How easy or how difficult was the scenario to understand?" on a five-point Likert item question from "Extremely difficult" to "Extremely easy" (Q3). The second part of the survey, invited participants to closely inspect the features depicted in the scenario (Q4). We provided a ’zoomed’ in version of the storyboard discussing the technology features and asked participants to indicate whether they like, actively dislike or feel neutral about each of the features in the scenario (see Figure 3). The final part of the survey, asked participants to consider all nine scenarios, "Consider all the scenarios you saw: Which of the futures envisioned is desirable and which less so?" and drag and drop at least two as their favourite (that they’d be willing to consider incorporating in their parenting) and place them on the "I love this box" and at least two that they dislike (that raise concerns/they’d find worrisome and would avoid using to support their parenting) and place them on the "Please don’t ever do this to me box" (Q5).
Figure 3: Like/Dislike rating example (Q4).
3.3.2 Participant Demographics.
Participants (n=92), were recruited through Prolific1 an online survey research platform and paid at a £10.03 per hour rate for their time. In total, participants took between 7-50 minutes to complete the survey, with a mean completion time of 19 minutes. To be eligible for the study, participants were required to be (1) over 18 years of age, (2) fluent in English, (3) have at least one child aged between 4-10 years old and (4) be based in the United Kingdom (U.K.). The relevant ethics approvals were obtained through the university ethics board and all participants signed consent forms prior to any data collection.
3.3.3 Data Analysis.
To analyse the quantitative data we examined the distribution of opinions that participants expressed through their survey responses. For example, this involved examining participant attitudes (i.e., agreement or disagreement) towards the technology’s potential inclusion in their parenting practices; the likelihood of using the technology; and overall sentiment (i.e., like, dislike or neutrality) concerning specific functionalities (features) as seen in the scenario. The scenarios were intended to be research probes for speculation, rather than independent variables; as a result, we do not make direct comparison between the example technologies in our analysis. Qualitative analysis was conducted using affinity diagramming [220]. We structured the responses to the open-ended survey questions into clusters which represented recurring patterns. Considering the diverse data and moderate volume, we decided to adopt this pragmatic approach as suggested by Blandford et al. [49]. In the case that more than five participants mentioned a particular theme, we have provided the exact number of participants instead of their individual participant number. We account recurring themes and concepts with direct quotes and paraphrases from our participant data.
4 DESIGN OF THE CONCEPTUAL FRAMEWORK (RQ1)
4.1 Overview
In this section, we present a summary of the conceptual framework outlining each dimension along with the relevant literature (for a comprehensive review of the framework refer to appendix A). As shown in Table 1 the framework characterizes nine parenting needs: Reflection, Self-monitoring, Knowledge Acquisition & Implementation, Practice for Skill Acquisition & Transfer, Dealing with setbacks, Communication, In the moment support, Habit Formation, Conflict Resolution; as well as six technology mechanisms (see Table 2) that might support them: Psychoeducation, Feedback, Parent Surrogate, Decision Making Support, Prediction to explore possible outcomes and Coaching. In what follows, we illustrate how we used the framework to develop our nine fictional scenarios by outlining the combinations of dimensions for each scenario and delivery mechanism (see Figure 4).
Figure 4: The table shows the combinations of dimensions that are included in each scenario as well as the delivery mechanism depicted in each scenario.
| Identifying parenting needs | ||
|---|---|---|
| Reflection. Parental reflection is essential for promoting desired behaviors, appropriately addressing children’s feelings and needs, and foster emotional well-being and resilience [14, 85, 325]. Reflective competences are multilayered [310]. As such, engaging parents with diverse backgrounds and experiences in meaningful reflection remains a challenge [187, 300]. | Self-monitoring. Training parents in self-monitoring (i.e., observe and track their feelings, reactions, behaviors, thoughts, and parenting activities) [136, 184] can help improve family functioning by enabling parents to identify their own vulnerabilities and navigate parenting challenges more effectively [251, 297]. | Knowledge Acquisition & Implementation. Supporting parental knowledge on child development and positive parenting strategies improves their ability to communicate effectively, set appropriate expectations and respond to challenging behaviors [56, 170, 246]. By gaining and applying knowledge, and witnessing positive outcomes, parents can enhance their self-efficacy and confidence [177, 360]. |
| Practice for Skill Acquisition & Transfer. PT is effective for skill acquisition (e.g., [121, 181, 231]). Through regular practice and application of skills parents develop a repertoire of responses to various parenting challenges (e.g., limit setting, communication and discipline) – improving the parent-child relationship [140, 182]. However, parents need support with consistently practising and applying the skills in their daily lives [122, 224, 334]. | Dealing with Setbacks. Experiencing adversity can disrupt parenting abilities, increase stress, and lead to child behavioral difficulties [23, 139, 331]. To increase resilience and help parents deal with setbacks means supporting parents to recognize that past skills can be applied to future challenges [223, 311]. In this way, increasing preparedness towards possible setbacks is a way to compensate potential setbacks [289, 339]. | Communication. Family communication and socialization processes are pivotal for children’s social development [193, 194]. Communication training helps parents understand and respond to their children’s needs, emotions, and behaviors [201, 295]. However, less focus has been paid to support co-parenting relationships—that is the collaboration and communication between parents—an important predictor of family functioning and child well-being [96, 158, 236]. |
| In the moment support. PT interventions aim to extend behavior change beyond the training setting to new situations and circumstances [40, 255]. While PT programs are effective, they have not always shown success in generalising skills to the home or impacting child behavior in the long term [61, 316]. As such, training parents "to generalise" skills [86, 334] in daily life through in-the-moment,situated interventions [326] can remove the transfer barrier and support natural learning opportunities that fit into everyday routines[82, 355]. | Habit Formation. Supporting parents in establishing family routines helps structure children’s environment whilst creating stability (e.g., establishing bedtime routines to reduce sleep problems) [131]. This helps children develop self-regulatory skills by teaching them that events are predictable and there are rewards for waiting [153, 272]. Finally, habits established in childhood can influence a child’s lifelong behaviors, shaping their approach to responsibilities and relationships [261]. | Conflict Resolution. Marital conflict is typically categorised as destructive (e.g., violence, threats) [95, 233] or constructive (e.g., promoting positive resolution and communication) [97, 148, 233]. Destructive conflict heightens the risk of child adjustment issues, while constructive conflict improves positive development [119, 263]. Training parents to resolve conflict constructively can support the development of children’s adjustment, social skills and overall well-being [166, 336]. |
Table 1: Summary of the Conceptual Framework for parenting needs, including dimension descriptions and related literature.
| Identifying role for technology | |
| Psychoeducation. Providing parents with knowledge of child development stages and positive parenting practices is crucial for promoting positive outcomes in children’s health, education, and well-being [182, 271]. However, for timely and appropriate information delivery [127] it is pivotal to understand the needs and preferences of various parent groups (e.g., high vs. lower Socioeconomic status (SES)) [34, 241]. | Feedback. Feedback in PT is an essential component for enhancing positive parenting practices [182, 353]. Training techniques include modeling, reinforcement, and correction [121, 313]. Programs typically use delayed feedback [178, 179] which however, effective for parenting and attachment [89, 299] limitation in access (due to its personalized nature and reliance on expert support) persist [173]. |
| Parent Surrogate. Assistive technology, such as artificial companions (AC) or social robots, could provide an alternative solution for parenting support (e.g., [180, 364, 368]). These intelligent companions can perform various tasks at home, including communication, information exchange, and care-giving [137, 154]. By acting as a "surrogate," technology can automate low-level parenting tasks like homework support and meal planning (e.g., [9, 320]), alleviating parental pressures. | Decision Making Support. Parents face numerous decisions when it comes to caring for their child’s well-being (e.g., deciding how, when, and where to seek support; which parenting strategies to use) [69, 167, 242]. While some decisions can be planned for, unexpected situations can increase emotional challenges [32, 252]. As such, supporting parents deliberative decision-making is pivotal in fostering effective child rearing practices [145, 164]. |
| Prediction to explore possible outcomes. Parenting and parent characteristics can predict childrens’ problem behavior [7, 64, 260]. Various models to predict parenting behavior have been developed (e.g., [26, 33, 324]) which (in most cases) suggest the determinants of antisocial and aggressive behavior in children. However, these models carry significant limitations (e.g., accuracy) due to limited data. Technological advancements (i.e., ML) could create more accurate models of parenting determinants and predict/prevent behaviors that cause child maladjustment [75]. | Coaching Coaching parents during parent-child interactions is an important mechanism of change [313]. Coaching utilises a more non-directive approach in supporting parents behavioural changes [138] treating parents as collaborative partners rather than passive recipients of expert advice [288, 341]. The coach helps the learner (parent) reflect on their actions and practices, evaluate their effectiveness, and develop a plan to improve their skills and use them in the future [256, 345]. |
Table 2: Summary of the Conceptual Framework for technology mechanisms, including dimension descriptions and related literature.
4.2 Putting the Framework into Practice
4.2.1 Creating nine futures using the framework.
We used this framework as a conceptual model to facilitate design thinking for the envisioning of our scenarios i.e., emerging technologies that might support parenting. It offered an explanatory approach that can be effectively used in the early stages of research and ideation. Specifically, our scenario storyboards depict an AI function (e.g., NLP, sentiment analysis, conversational interfaces etc.) that could deliver parenting support. Figure 4 illustrates the dimensions and delivery mechanism included in each scenario. Each scenario acts as probe in which participants react to. We intentionally focus on the design mechanisms/features of the technologies envisioned to better understand the potential value of the suggested AI application as well as identify the technologies that resonate with parents. We first developed our scenarios as text based narratives amounting to 1,5 page each (length of text). To make the scenarios easily digestible and gather participant reactions, we developed the narratives into simple 3-step visual examples (refer to supplementary documents for all scenario storyboards). Each scenario/storyboard is composed as follows: first image, introduction of parenting need/context; second image, example of specific support/technology features that address the parenting need in question; third image, outcome/impact on family and (parent or child) behaviour. In this way, we were able to preserve the core components of the text-based narratives and translate them into concise visual examples.
4.2.2 Aesthetics and design rationale.
We aimed to produce scenarios that were novel and clearly situated in the future, yet realistic enough for participants to easily envision them happening in their own lives – this juxtaposition enabled the immersion with the provocations more deeply. Each scenario imagined a somewhat different technological capability/delivery mechanism (see Figure 4). Drawing inspiration from Wong et al. [361] we depicted each scenario using a variety of media, such as newsletters, advertisements, FAQ pages, testimonial pages, landing pages, and product pages. We also presented our designs in different contexts, such as on a desktop or a laptop, on an a mobile app, a newsletter spread, and in billboards as adds. We constructed these provocations using free stock images and vectors. In addition, the first author designed the landing pages, UI’s, advertisements and technologies using Figma, Adobe InDesign, Illustrator, Photoshop and Canva. For one of the scenarios (Scenario 5) we used Picsart, an open-source AI image generator as we were unable to source online the imagery we had envisioned. We deliberately designed our scenarios as high-end products/technologies to increase familiarity and easily immerse participants in the envisioned futures.
5 FINDINGS
In this section we discuss the key findings from our survey. We report how participants perceived and reacted to AI assistants in the context of parenting support. In what follows, we report our quantitative findings and supplement them (where relevant) with the most notable themes that emerged through our analysis of the qualitative responses. To protect anonymity, participants are referred to by using ’P’ for participant, followed by a participant number.
We present the results in three sections:
(1) | First, we start by providing a general overview of participants attitudes and reactions across all nine scenarios. In addition, we discuss perceptions of believability and relatability of the futures (RQ2). | ||||
(2) | Second, we discuss four scenarios that participants reacted—in majority—more positively to provide a more detailed perspective about the potential ways the envisioned futures could impact parenting life. We discuss, the key themes that emerged from each design concept highlighting both the tensions and (perceived) qualities of the envisioned technologies (RQ3). | ||||
(3) | Finally, we discuss the specific features within each of the nine scenarios to capture parents perceptions about the particular mechanisms envisioned. | ||||
5.1 How do parents react to design fictions of AI assistants for parenting support (RQ2) and what do parents perceive as potential benefits and frictions exist in this emerging design space (RQ3)?
5.1.1 Consider the scenario you just saw: Would you like to live in this future and how likely are you to use it?
Participants were presented with the scenarios in random order – their responses to the proposed futures varied. When asked to indicate their reactions to the scenarios on a 5-point Likert scale (see Figure 5a), the largest proportion of participants selected the option: ’I am open to the proposed imagined technologies and acknowledged their possible, positive implications’ received the most responses (selected by 29.95% of participants). This was closely followed by the option: ’the technologies unlikely to be helpful for me’ (selected by 29.21% of participants), who expressed reservation, but did not rule them out entirely. Almost a quarter of all participants (23.72%) outright rejected the scenarios as possible future solutions, while a smaller percentage (12.84%) expressed some confidence in utilising the envisioned technologies. Lastly, a small but notable group (4.28%) indicated a willingness to explore the AI assistants further (i.e., would "Definitely" try them to support their parenting).
Figure 5: Parent’s ratings of the individual scenarios
Participants provided various reasons for both their positive and negative sentiments toward AI parenting supports. Specifically, some participants recognised the potential benefits of AI assistants in providing suggestions, reminders, supporting positive habits, and assisting with their child’s academic efforts, "I quite like the idea that it can work as a tutor of some sorts and help children engage better in their homework tasks" (P11). Others, expressed that they could envisage that the potential AI assistants could be helpful in serving as virtual companions or support systems, particularly in cases involving single parents or situations where an additional perspective might be needed, "I like that it will listen and give support where needed, sometimes in the moment you need another person to help break up a situation if you and your child are both frustrated" (P30). Interestingly, participants expressed that the designed scenarios appeared believable (refer to Appendix B for the response distribution graph) suggesting that they deeply engaged with envisaging its role in their lives (i.e., the parent reviews within the storyboards offering further social proof and validation of the tool’s (potential) effectiveness: "It seems like it has helped some people based on the reviews so it could be good for others" (P54).
Across the data, participants recognised the potential benefits of AI assistants, but they also expressed concerns regarding the potential impact of technology on genuine parent-child interactions and the bonds that parents form with their children. This was reflected in participants’ hesitation to strongly accept (or reject) the solutions, often indicating that they were "Slightly likely" (30.67%) to use the envisioned technologies. In particular, participants, expressed concern that the technology might undermine the authenticity of parenting experiences and become "too involved...[and] take away valuable parent time with children if parents relied on it" (P72). Additionally, participants expressed discomfort towards the concept of AI assistants taking on parenting roles. In particular, they voiced concerns around AI undermining parental authority, interfering and altering family dynamics; as well as promoting "lazy parenting" (P66). Lastly, we observed that participants expressed privacy concerns and the potential negative impact of constant monitoring and data collection. Specifically, participants expressed fear that monitoring through AI technologies could negatively impact on their confidence as parents while "it could cause your child to become unhappy [if] you are watching them all the time" (P19). This negative sentiment was also evident across participant responses towards adopting the envisioned technologies, with 54.39% (sum of all scenarios) considering it "Extremely unlikely" (see Figure 5b
In summary, participant responses reflected an interplay of optimism and apprehension regarding the use of AI assistants for parenting support. In order to describe in more detail how these concerns and opportunities manifested, we next focus on four scenarios that received predominantly positive reactions from the participants, i.e., the scenarios that participants could most likely envisage being part of. We unpack the key themes that emerged from each design concept, highlighting the tensions, opportunities and perceived qualities of the envisioned technologies.
5.1.2 Four plausible futures.
We selected four scenarios that received the most positive responses (i.e., assigned a rating of possibly, probably and definitely). This approach enabled us to scope down the scenarios that parents expressed they could envisage, thus producing the most detail about potential ways they could impact on parenting life (RQ3). Specifically, the following four scenarios generated the most positive ratings across all participants: ParentSage (64.13%), SerenityGuidance (60.87%), ParentSense (58.70%) and HarmonyHub (47.82%). These scenarios were also separately ranked as most appealing in a later survey question that asked participants to rate all nine scenarios (see Figure 6), which reinforced the prominence and plausibility of these four scenarios for parents.
Figure 6: Parent ratings when presented with all nine scenarios (Q5).
The first scenario explored parental perspectives towards a wearable app designed to provide feedback on parenting practices, track progress as a way of increasing self-awareness of skill development, and offer helpful reminders to increase engagement with learning (see Figure 7). The majority of participants (64.13%, N=59) indicated a willingness to try the technology, while less participants (35.87%, N=33) expressed negative opinions and skepticism towards the envisioned future. Based on our analysis of the open text field responses, we identified two overarching themes: (1) Potential benefits and practical applications; and (2) Privacy and judgment concerns.
Figure 7: Scenario 1: ’ParentSage’ storyboards.
Some parents expressed that they were open to receive support and guidance by AI assistants as they acknowledged that being a parent is challenging and there’s always space for improvement "I feel I’m not the best parent I could be, and welcome support to improve" (P07). Specifically, participants cited personalised feedback, skill-mastery and evidence-based advice as the primary factors that would motivate their uptake of the envisioned app. However, despite the potential benefits, participants also expressed reservations about whether AI assistants could truly enhance the parenting experience, questioning the added value and purpose of these tools. For instance, participant (35) expressed caution about AI becoming too involved in family life and detracting from the natural parenting experience "I don’t like the idea of having to be reminded to be a parent as this should be natural". Finally, participants expressed worry about the impact on their privacy and autonomy as parents. In particular, participants expressed discomfort with the idea of constant monitoring and feedback; and emphasised the importance of maintaining control over their parenting decisions as "this feels overly judgmental and will just leave people believing that they are bad parents" (P56).
The second set of storyboards probed participant attitudes toward a spoken diary app that encourages parental emotional release. Using advanced speech-to-text technology, the AI assistant identifies potential knowledge gaps, provides personalised guidance and supports data-driven decision-making (see Figure 8). The majority of participants 60.87% (N=56) indicated a willingness to try the technology, while a substantial minority 39.13% (N=36) expressed negative opinions towards the envisioned future. After examining the participant responses provided in the open text field, we identified three overarching themes: (1) Supporting parental emotional well-being; (2) AI assistants for self-improvement and learning; and (3) Concerns around AI emotional support.
Figure 8: Scenario 2: ’SerenityGuidance’ storyboards.
Parents reported that they saw the benefits of the scenario as they recognised the emotional challenges that come with raising children, and the need for support in managing their own emotions as well as their child’s. In particular, participants expressed that they could envisage AI for emotional support as a potential solution for those moments when parents need to vent, seek advice, or simply find comfort. For instance P67 reflected on their need for regulating their emotions: "I am aware that I struggle with frustration and that my family around me find that challenging. They don’t know how to deal with it, and being able to vent without being challenged would be welcome". Participants also perceived the envisioned AI assistant as a tool that could complement their existing parenting style, given its less intrusive role towards offering personalised support and guidance, e.g., P83 commented: "Everyone has some frustrations (emotions) to voice out, if this AI can keep a diary of these emotions learning from them with the aim of improving lifestyle will be great".
While most participants expressed positive sentiments toward the envisioned technology some, voiced reservations in trusting AI for parenting advice and emotional support. For instance, some participants worried about the accuracy of the advice provided by an AI system, given that it may not fully understand the nuances of individual family situation. Further, in terms of privacy, participants were keen to understand about the security measures in place to protect their data, and raised concerns around "[...]safeguarding implications. If diary entries are being recorded they cannot be kept confidential if there is a safeguarding issue. What are the protocols for this?" (P41).
The third set of storyboards probed parents’ attitudes towards Emma, an app based virtual assistant (or chat-bot) that delivers personalised information and support to frustrated parents (see Figure 9). The survey data suggested that participants mostly expressed a neutral-to-positive attitude towards the scenario (54/92 or 59% of participants) expressed that they could envisage using the technology within this scenario. Conversely, 41.30% of participants (38/92) indicated some level of disagreement or uncertainty in using the technology. Based on the open text field responses in the survey, we were able to explore in more detail what these moderate attitudes entailed. We grouped participant attitudes into three main themes: (1) Perceived benefits of AI; (2) Concerns about AI interference in parenting; and (3) Preference for human interaction.
Figure 9: Scenario 3: ’ParentSense’ storyboards.
In terms of the perceived benefits of AI, participants acknowledged the convenience and potential advantages that the technology could offer. Namely, they listed benefits in the technology offering tips, feedback, and stress management techniques, and expressed that the child-centered features could support their parenting by being more aware of child mood, e.g., "understanding children’s mood will help me a lot in managing their needs" (P24).
However, some participants also expressed concerns over privacy. This related to the technology listening in on their parent-child conversations; (potentially) disclosing sensitive personal information; and data breaching. As seen in the quantitative data a significant portion of parents expressed negative reactions and explicit concern towards this scenario. For instance, participants expressed that by relying on an AI chat-bot, they may become fixated on external opinions, undermining their confidence as parents. Similarly, parents expressed that the chat-bot might impact on their decision-making skills, as expressed by one participant who stated: "I feel like this would get in the way of parenting. I wouldn’t be able to make decisions myself and have to rely on an app to tell me what to do" (P75). Participants also explain that they wouldn’t want to receive advice from an AI since "it is not the job of an AI to parent children. It seems like a tool for laziness" (P30). Finally, parents emphasised the importance of human interaction and support in their parenting. They explained that parents should seek help and advice from real people or experts rather than relying on AI. Across the data, participants suggested that parenting is a deeply human endeavor, and as such, "children need their parents to take them through life, not AI" (P66).
The fourth scenario envisioned a parenting platform that promotes effective conflict management, open communication, and positive family functioning by offering feedback, engagement time, parenting performance reviews, child’s point of view, and other functions (see Figure 10). Almost half of all participants expressed positive attitudes towards the envisioned technology (47.82%, or 45/92 participants), with just over half of participants (52.17%, 47/92) who expressed hesitation and disagreement. Participants’ open text responses were grouped into three main themes: (1) Positive implications of AI in resolving conflict; (2) Concerns about AI interference on family dynamics and (3) Privacy and autonomy concerns.
Figure 10: Scenario 4: ’HarmonyHub’ storyboards.
Approximately one third of all participants expressed interest in specific functions displayed in the scenario (e.g., conflict resolution feature) and recognised the (potential) benefits of the envisioned technology. For instance, participants acknowledged the potentially helpful role of technology in creating a platform for dialogue, fostering greater understanding among family members, and facilitating the exchange of diverse perspectives, "I love the child’s point of view feature. I believe it’s important for kids to safely express their feelings and thoughts" (P19). Added to this, participants recognised value in having access to better conflict resolution strategies and appreciated the equitable approach of involving the entire family in this process.
However, participants also voiced concerns and hesitation about the impact of the AI interface on family dynamics. This related to uncertainty about how the system would practically operate, or what effect it could potentially have on interpersonal communication in the family – with some being "...on the fence with this" (P20), expressing doubt and apprehension about the capability of AI to deliver practical advice and support. Participants also expressed concerns regarding the potential for the technology to create disagreements or rifts among family members, "reports on each parent are going to make for divided parents" (P04). Furthermore, some participants perceived this scenario as dystopian and criticised the assumed necessity of technology to act as the intermediary during sensitive human interactions, "If conflicts cannot be resolved other than with the use of AI, then we truly live in a scary world. AI analyzing human interactions and giving advice to humans [on] how to be human? It’s like a prologue to the Terminator movie, but unfortunately in the real world this time" (P31). Finally, as in the case of scenario 1, participants expressed worry about the impact on their privacy and autonomy as parents. Specifically, participants voiced concerns about the system tracking and monitoring their parenting time, fearing it could turn parenting into a measurable task and cause unnecessary pressure to meet specific (parenting) quotas. As such, participants expressed mixed views about this scenario, with multifaceted potential benefits and concerns for the technology’s role within their family ecosystem.
5.1.3 Parent reactions to specific AI features.
In contrast to our previous analysis, where we examined parents’ perceptions of the technology at a scenario level, this section considers parent reactions to specific features within each of the nine scenarios. We identify participant perspectives relating to the specific mechanisms within the scenarios. We then identify trends in parent opinions relating to cross-cutting features across all nine scenarios (comprising 42 features total, refer to supplementary documents for full ranking) parents prioritised.
We asked participants to pick the features that they found most helpful within each of the nine scenarios. This was done to gain insights into the mechanisms that parents would find helpful, regardless of their positive or negative reactions towards the specific scenario. Surprisingly, the six features that were ranked highest aligned with the ranking of the scenarios. As seen at the table (see Table 3), the features that resonated with parents were grouped into two categories: emotion-focused (i.e., calming techniques, reflection, emotional release, child’s point of view) and personal growth/skill-development focused (i.e., personalised feedback, mastering new skills). These findings are consistent with prior research highlighting the need for supporting parents emotional [84, 250] and skill-based [122, 196] competences.
| Feature | Description of Feature | Positive rating (N) |
| The app suggests techniques like deep breathing to empathetically manage tantrums and exercises to improve communication between parent-child. | 56 | |
| Emotional Release | Speak your frustrations, fears, and worries without judgment, and experience a sense of relief through our spoken diary feature. | 56 |
| Master New Skills | Trust in your ability to grow as ParentSage provides personalized tasks, such as creating a special mealtime routine. Armed with practical advice and support, witness the transformation unfold. | 55 |
| Child’s Point of View | Channel where children are encouraged to express their feelings and thoughts on specific parenting approaches or situations. This allows parents to understand their children’s needs and preferences better. | 52 |
| Personalized & Reliable Feedback | Using an advanced algorithm and evidence-based research, our system provides objective insights that are tailored to your parenting style and challenges, helping you make informed decisions for your child’s development. | 51 |
| Reflect & Grow | Parents input their daily routines, interactions with their children, and notable events or challenges. It also includes a mood recording feature using a simple emoji-based system. Finally, guided reflection is offered to gain insights into reactions, biases, and respond compassionately. | 50 |
Table 3: Five most favourable HCI features for parenting support across 9 scenarios (Q4).
In terms of emotion-focused mechanisms, participants expressed a variety of reasons why this was a desirable feature. For example, the majority of participants ranked the calming techniques and suggestions feature found in ParentSense (scenario 3) and emotional release feature found in SerenityGuidance (scenario 2) as most desirable (56/92 and 56/92 participants). Participants recognised the importance of having tools and systems in place that support their self-regulation skills when dealing with child-related difficulties. For instance, P82 explained that AI could be a valuable tool in supporting them during challenging situations, "the calming techniques feature is great help to manage difficult situations". They also acknowledged the importance of features that assist them in effectively expressing and managing their own emotions, "getting good emotional balance after each day’s work will help for good mental well-being" (P44). The second most preferred feature (55/92 participants) focused on skill development and mastery, e.g., found in ParentSage (scenario 1). This worked by supporting parents in accomplishing specific tasks related to their parenting objectives. Participants explained that such tools could be valuable in improving their parenting skills and expressed willingness in trying the technology, "because it offers personalized features and assists in mastering new skills, it is something I will like to try" (P39). Separately, our findings suggested that it was important to incorporate the child’s perspective when designing to support parent skill development and family dynamics. This was indicated by participant rankings of the child’s point of view feature (52/92) found in HarmonyHub (scenario 4). In particular, participants viewed this function as a means of increasing (child behavioural) understanding, facilitating family dialogue and promoting healthier interactions, "I think this may be a good idea as the whole family has input into it so can be seen as a tool for your family to resolve issues" (P29). Participants further indicated they wanted access to parent-centred functions to complement their existing parenting strategies as expressed through their ranking (51/92) of personalized & reliable feedback feature found in ParentSage (scenario 1). This worked by offering personalised guidance and practical advice. Specifically, participants cited that they often needed access to additional knowledge and guidance for improving their ways of parenting. This was conveyed by P12 who expressed: "I believe it could be useful in aiding people who have a lack of knowledge on how to be a parent. I like it as a guide". The last feature that was highly ranked by participants was the reflect and grow feature (50/92) found in SerenityGuidance (scenario 2). Participants emphasised the need to voice their frustrations, receive feedback and engage in self-reflection whilst preserving their confidence, "I like the idea of a diary that can be used to reflect and help [...]" (P61). Finally, we also observe a trend in terms of features that participants appear to dislike (which were more varied across functions). In particular, participants seem to dislike the monitoring (55/92) feature, which was consistently described as undesirable across all scenarios. Specifically, participants expressed concerns about constant AI monitoring, which they perceived as a form of surveillance, "Its listening and monitoring everything. There is no privacy" (P08).
5.2 Summary
The aim of the scenario probes was to explore with parents a set of possible socio-technical design directions with emerging AI-mediated modalities, and to identify how parents reacted to possible scenarios when designing AI assistants that might support families. Participant reactions at the level of the scenario and more specifically, at the level of the feature captured a mixed picture of both optimism and concern. Participant responses ranged from open enthusiasm about the potential benefits of augmenting and building on their existing parenting practices with AI-mediated support, to skepticism and concern relating to privacy, the AI technology’s impact on genuine or meaningful parent-child interactions, and on family dynamics.
A significant portion of participants recognised the potential advantages of AI assistants, expressing that they could envisage supports in terms of providing suggestions, reminders, and supporting positive habits, as well as acting as virtual companions, support systems or emotional relief mechanisms. Others, did not share this positive outlook with many participants expressing reservations about technology’s potential to undermine the authenticity of parenting experiences and disrupt family bonds. Participants expressed concerns about AI taking on parenting roles, potentially undermining parental authority. Privacy concerns and worries about constant monitoring and data collection further contributed to the apprehension towards (potentially) adopting AI parenting supports. Our findings highlight the need for careful consideration of the potential benefits and risks associated with AI assistants in parenting, as well as the importance of addressing privacy and ethical concerns.
6 DISCUSSION
In our paper, we examined the possible opportunities for AI technology to be utilised in the context of designing for parenting support. This was prompted by limited prior guidance and research on how AI-enabled technologies have incorporated and prioritised different dimensions of evidence-based parenting approaches. We achieved this by first, creating a theoretically grounded, conceptual framework that incorporates different areas of parenting needs and possible technology mechanisms, based on a review of the parenting literature and discussions with psychology experts. Second, based on this framework, we then created a set of nine fictional scenarios for how AI assistants could potentially offer situated parenting support, and finally, tested these scenarios through an online survey study with parents to investigate their initial reactions to the envisaged scenarios. We found that parents expressed mixed reactions to the potential benefits of AI through the scenarios, as well as specific concerns relating to family dynamics and privacy. Through our work, we contribute i.) conceptual knowledge about the parenting need areas and technology-based mechanisms that potential AI technologies for parenting should consider, and ii.) empirical research on parental reactions to the envisaged future scenarios. Focusing on these two contributions, we discuss how our findings raise important considerations for what AI-enabled parenting support has focused on to date and what this means for future research considerations in this area.
6.1 Research Agendas for AI and Parenting Support
Prior work that has explored designing for AI-enabled mental health support has focused on the possibility of using AI and NLP when designing for population-level mental health interventions [134, 307], as well as targeted areas such as diagnosis and risk detection [106, 254, 257, 343]. However, one focus of prior work to date has been on studying the technical feasibility of these technologies with less consideration for the real-world clinical impact [2, 78, 129]. Consequently, there has been a disconnect between AI development and understanding the intended impact on people in the context of their everyday practices. In the parenting context, some prior research has taken a user-centred approach, which has been helpful for advancing understanding about some of the challenges with delivering effective parenting support [109, 197, 269, 335, 337]. For example, focusing on the delivering of situated support, low parental engagement and a lack of personalisation of existing parenting programmes [28, 103, 176, 302, 323]. However, our study has shown that designing for parenting intervention supports is complex, as it is not always easy to identify the components that can be most effectively supported by technology [168, 211]. This is because individual components (e.g., reflection, self-monitoring etc.) do not always clearly align with technology-enabled delivery mechanisms (e.g., feedback, decision making etc.). Through our conceptual work, we have shown that as well as a need for a user-centred approach, there are a breadth of parenting need areas that are yet to be considered. For instance, prior studies have focused on supporting specific application areas that include: parent-child communication [35, 141, 197, 268, 329], child and adult emotional states [8, 188], and specific daily practices (e.g., motivating children to tidy their rooms, reading habits and specific learning goals) [31, 133, 142, 239, 303, 352]. However, our research suggests that within each of these and many other application areas, AI research should also consider the design of parenting supports on a more granular level. For instance, when designing for parent-child communication, future designs should build in ways of supporting parents with their skill development and maintenance across a range of dimensions that include fostering reflection, self-monitoring, knowledge acquisition and implementation and other areas of need. Our framework is a conceptual model that can help HCI and interaction design researchers to envision what AI assisted parenting support might look like across a range of dimensions. Its primary focus is to guide thinking around the design of emerging technologies that can support the acquisition of parenting knowledge and skill development through the integration of emerging technologies. The framework is not intended to be prescriptive or rigid in nature, but offers ideas for building on current work on AI and parenting through an explanatory lens.
6.2 Parent Perceptions and Implications
Our findings highlighted the importance of considering human factors early on by identifying how AI is interpreted by parents, and how these interpretations might influence their uptake. Based on parent reactions to the scenarios, our findings showed that AI is only one component of a much larger socio-technical system. Implementing AI in families means prioritising parents’ expressed needs as priorities for the ways that AI can and should offer support. That is to say, just because an AI solution is possible does not mean an AI solution is needed. In what follows, we examine potential opportunities and challenges based on what participants in our study expressed. Specifically, based on parent feedback, participants saw value in functions that can provide personalised support, emotional support, and conflict resolution; suggesting that designing to support healthy communication within families is an important direction. However, participants also expressed reservations about AI being fully embedded in–and taking over some actions within–the family ecosystem. For example, participants often interpreted the agents in the scenarios as excessively intrusive and expressed concerns about their potential interference with their personal parenting choices. Consequently, it may be more appropriate to position AI supports as systems that deliver alternative perspectives on parenting matters rather than imposing decisions. Similarly, participants also expressed concern over the trustworthiness of AI systems due to the AI’s limited understanding of the nuances of individual family situations; and doubt about AI’s ability to provide valuable parenting advice. To this end, the transparency and trust of AI systems should also be viewed as a part of the design process [318]. Our participants also expressed concerns regarding potential negative outcomes that might arise from the AI assistants featured in our scenarios. Some participants suggested the negative outcomes they envisaged could likely outweigh the potential benefits. In particular, parents were less accepting of scenarios that included agent roles as surrogates and more accepting of scenario designs with empathetic listening assistants. This builds on prior work that has considered agent roles and how people are motivated to use these technologies, and what people perceive about the technology’s expertise and intelligence [253, 321, 347]. Our findings suggest that future designs should carefully consider the kinds of roles and responsibilities we assign to AI assistants as this could determine how likely the technology is to be adopted and what the implementation and outcomes could look like. Lastly, parents also expressed concern about AI potentially undermining parental authority and altering family dynamics. This was evident in examples where parents objected to the use of AI as an intermediary in situations where two parents might hold different opinions. Considering prior research that has started to consider how AI might ’recognise’ or attribute mental states (e.g., in the context of studying artificial Theory of Mind) (c.f., [203, 204, 216, 348, 358]) or predict behavior based on mental states, caution is needed in the parenting design domain, given the need for parent agency. As such, rather than taking a role in decision-making, AI could amplify [319] or complement the effective strategies that parents are already using to support them in decision making about inter-parental relationships or parent-child interactions.
7 LIMITATIONS AND FUTURE WORK
Several limitations of this work should be noted. We used Prolific as a first step in identifying future directions for AI assisted parenting visions. By using a single recruitment platform and limiting participation to people in the U.K., the perspectives represented in our study are limited to those of a relatively small number of people, and may not reflect those of demographics not represented in our sample. In addition, the implications of using a narrow sample, who are paid to take part in research responding to web-based surveys may be more accepting of technological interventions than parents who do not respond to online surveys, resulting in selection bias. As such, additional work is needed that involves a wider-demographic of parents to a get a more holistic perspective of parent attitudes towards AI assisted parenting technologies.
Second, we deliberately created conflicts in our designs to balance unforeseen friction [18]. Specifically, our scenarios included provocative elements (e.g., depicting technology as a surrogate) and positive undertones (e.g., positive reviews of other parent perspective) to elicit varied reactions [52]. Consequently, our findings showed that this approach effectively prompted both negative and positive reactions from the participants while also invoking their thought processes, values, and needs. However, we acknowledge that the inclusion of mostly positive fictional user reviews may have influenced participant judgement of the scenario (which we also have discussed in the findings). To address this, future work could include more impartial reviews from existing rating platforms (e.g., trust-pilot or google reviews) and incorporate negative, neutral, and positive reviews across the scenarios.
Finally, there are several ways this study can be extended in the future. First, future work might expand one or more design concepts (as ranked by participants) and conduct an additional survey study that reflects iterated scenarios based on parents sentiments to identify further opportunities and limitations. Second, many participants expressed concerns toward AI assisted parenting. To better understand why parents felt this way, engaging in co-speculation activities might provide further insight as well as uncover additional opportunities and concerns. Third, while our findings provide some indications on parental perceptions of AI assistants, additional research is needed to explore how we can help parents understand and use information provided by AI as to support their parenting efforts. To investigate this in context, a complementary qualitative study using situated methods could provide further insights, validate, or modify some of our findings.
8 CONCLUSION
In our paper, we examined the possible opportunities for AI technology to be utilised in the context of designing for parenting support. We used design fiction as an accessible vehicle through which participants experienced future design concepts and articulated their own views of the future of AI assisted parenting. Our goal was to identify the possibilities and implications of these emerging AI technologies (i.e., NLP) that might support families. Our findings identified mixed reactions to the potential benefits of AI as well as specific concerns relating to family dynamics and privacy. Our paper contributes to the field of HCI in two ways. First, it provides conceptual knowledge about the parenting need areas and technology-based mechanisms that potential AI technologies for parenting should consider. Second, it offers an empirical contribution through parental reactions to the envisaged future scenarios.
ACKNOWLEDGMENTS
We would like to thank our participants for their time and the deep engagement with which they took part in the research process. This work was supported by the Engineering and Physical Sciences Research Council [grant number 2552175]; and a UKRI Future Leaders Fellowship [grant number MR/T041897/1].
A CONCEPTUAL FRAMEWORK
A.1 Identifying parenting needs
The ability of a parent to understand both their own and their child’s mental states is considered crucial within the parent-child relationship [85, 325]. Reflecting on one’s actions enables the development of desired behaviors, emotional control, and empathetic responses [14, 16, 118, 325]. Through reflection parents get empowered to recognise when their child’s behavior deviates from normative development, respond respectfully to the child’s feelings and behaviors, and nurture their emotional well-being and resilience [105]. However, there are varying levels of reflective competences [310] and ensuring that parents with different backgrounds, experiences, and abilities can engage in meaningful reflective practices remains a challenge [187, 300].
Training parents in self-management (i.e., self-monitoring, goal setting and problem-solving) can prove instrumental in supporting positive family functioning [297]. In particular, self-monitoring enables parents to (self) regulate through systematic observation and tracking of their feelings, reactions, behaviours, thoughts and parenting activities [184, 297]. Monitoring can provide a simple means to track progress (e.g., measuring parents responses to child behaviour, overall outcomes and family functioning) [46, 136] as well as readiness for different stages of intervention (e.g., the introduction of specific strategies to support regulation) [251, 294]. In this way, the promotion of effective parenting skills and the modification of parental behaviour can be established through supporting parents in adopting a self-regulatory approach [16, 294].
Parenting knowledge is essential for effective parenting (e.g., [30, 56, 170, 246, 360]) and it is positively associated with the quality of parent-child interactions, parental engagement in promoting children’s well-being and development, and the ability to make informed decisions[238]. Acquiring knowledge—about child development and parenting strategies—helps parents communicate effectively with their children, set appropriate expectations, and respond to challenging behavior [149, 170, 172, 264]. Lastly, by gaining knowledge, applying it in their parenting practices, and witnessing positive outcomes – parents’ self-efficacy and confidence in raising children improves [177].
The efficacy of parent training for skill acquisition is evident throughout the literature (e.g., [121, 181, 231, 332]). Through regular practice (e.g., homework) and application of skills parents develop a repertoire of responses to various parenting challenges (e.g., positive attention, limit setting, communication, discipline, and problem-solving) becoming more proficient in their interactions with their children [140, 182, 248, 308, 342]. However, to encourage adult learning, it is crucial to provide adults with opportunities to apply the information they are taught [224]. This includes supporting parents in consistently practicing and applying skills beyond the training context, reinforcing their use in daily life and transferring them to various situations [86, 122, 334].
Parenting rarely goes as planned [94]. Experiencing adversity in parenting creates obstacles that can disrupt parenting abilities, increase frustrations, parent stress levels as well as child behavioral difficulties[23, 139, 331]. Teaching parents how to deal with setbacks or adversity can substantially increase parents’ sense of competence, satisfaction (in their parenting) and resilience [44]. Training parents to be resilient—the ability to “bounce back” after a setback—means supporting them recognise that skills used in the past (to overcome setbacks) can be applied to future challenges [55, 223, 223, 311]. In this way, increasing preparedness towards possible setbacks is a way to compensate potential setbacks [289, 339].
Family communication and socialization processes provide the primary mechanisms through which children develop social competencies [193, 194, 306]. The communication and skills children acquire through the family ecosystem (or fail to acquire) will have an impact in the quality of their interpersonal relationships throughout their life [192].Parent training in communication support strategies is common in interventions (e.g., [4, 232, 295]), as effective communication helps parents understand and respond appropriately to their children’s needs, emotions, and behaviours [201]. Despite the emphasis on improving the parent-child relationship in many interventions [91, 132], supporting positive communication practices between parents (i.e., jointly setting parenting goals, reducing misunderstandings, minimizing potential conflicts) is equally important in creating a positive family environment [53, 96, 158, 234, 236].
The extension of behavior change from the training setting to new situations and circumstances is a universal goal of PT interventions [40, 231, 255]. Research has demonstrated the efficacy of PT programs however, when examining the extent to which those skills generalize to the home or the effect that they have on child behavior have not always proven successful – especially in long-term follow-ups [61, 316]. As such, explicitly training parents "to generalise" (i.e., instructing parents to use the skills in everyday situations) and reinforce the occurrence of generalised performance is necessary [86, 334]. In this way, supporting parents in the moment through the design of situated interventions [326] will remove the barrier of transfer and instead, support natural learning opportunities that fit into everyday routines [82, 326, 355].
Supporting parents in establishing family routines helps structure children’s environment whilst creating stability (e.g., establishing bedtime routines to reduce sleep problems) [131]. This helps children develop self-regulatory skills by teaching them that events are predictable and there are rewards for waiting [153, 272]. Finally, habits established in childhood can influence a child’s lifelong behaviors, shaping their approach to responsibilities and relationships [261].
Marital conflict is typically categorised as destructive or constructive [95, 233]. Destructive conflict (e.g., violence, aggression, and threats) leads to children’s negative emotions and (potential) adjustment problems due to lack of coping skills [98, 233]. In contrast, constructive conflict (e.g., promoting positive resolution and communication) fosters happiness and security in children, enhancing their problem-solving abilities and positive adjustment [97, 148, 155, 233]. Research consistently indicates that destructive conflict heightens the risk of child adjustment issues, while constructive conflict improves positive development [119, 263]. Therefore, training parents to resolve conflict constructively will prove beneficial in supporting the development of children’s adjustment, social skills and overall well-being [166, 336].
A.2 Identifying the role for technology
Parenting education programs typically seek to shape parenting attitudes, beliefs, and practices [182] by empowering parents with knowledge for informed decisions about their children’s health, education, and well-being [271, 287]. Understanding preferences among different parent groups (e.g., higher vs. lower Socioeconomic status (SES)) for parenting information sources is needed to enable tailored delivery [34, 241, 298]. In addition, parenting information can be accessed via digital media and internet applications providing up-to-date content and visually transmitted information delivered through various channels (e.g., podcasts and online forums) increasing parents’ self-efficacy and decision-making abilities [35, 66, 162, 275, 338, 346]. However, significant barriers persist in delivering the right information to the right people at the right time [127].
Feedback is a component used in Behavioral parent training (BPT) and includes techniques like modeling, reinforcement, and correction to increase positive parenting behaviours [121, 312, 313]. BPT programs typically use delayed feedback (i.e., during sessions when parents report attempts to practice skills at home); but in-vivo feedback during training sessions (i.e., therapist observes parent-child interaction and provides immediate feedback via earpiece) [89, 299] or video-feedback (i.e., filming parent–child interactions and reviewing the video with the parent) [178, 179] are also utilised. Despite positive effects in enhancing parenting and attachment [173], these approaches carry limitations in increasing access due to their personalised nature (e.g., video recordings of specific parent-child interactions) and reliance on expert support (e.g., interventionists or therapists actively participating in improving parenting behaviors) [173, 178].
An alternative solution to (parenting) support may be represented by assistive technology such as artificial companions (ACs) or social robots (i.e., devices that can perform various tasks at home, including daily communication, information exchange, and sharing intimate feelings) [137, 206]. These intelligent companions engage users by performing various roles, including nanny, nurse, peer, facilitator, or caregiver (e.g., [8, 150, 154, 229, 237, 266, 283, 364]). Research has explored their application in parent-child communication [364, 368], entertainment [180], and maintenance of children’s behavioral records [317]. In this way, we explore how ACs (e.g., chatbots, robots or conversational agents) might support parenting. For instance, technology could act as a "surrogate" and automate specific low-level parenting tasks (e.g., provide homework support and meal planning [9, 320]) to relieve parental pressures. Further, AC’s can improve children’s motivation and interest in learning [159, 228], encourage children to engage actively in communication [5], support the development of emotion regulation practices [326, 344] and consult with children about their concerns [43].
Caring for a child’s well-being and making decisions demands significant effort from parents. Parents are confronted with many decisions including how, when, and where to seek support [69, 80, 93, 242]; which strategies to use to effectively to address the issue at hand [167, 198]; as well as agreeing on parenting approaches between parents [94]. While some decisions can be anticipated and planned for, unexpected situations can intensify the emotional challenges parents face [32, 252]. As such, supporting parents deliberative decision making by taking into account their preferences, sharing knowledge and skills is pivotal in fostering effective child rearing practices [145, 163, 164, 287].
Parenting and parent characteristics predict childrens’ problem behavior [7, 64, 101, 260]. Several models to predict parenting behavior have been developed (e.g., [10, 26, 27, 33, 68, 259, 265, 324, 369]) which (in most cases) suggest the determinants of antisocial and aggressive behavior in children. However, to accurately facilitate prediction and prevent parenting behaviours—that contribute to child maladjustment—we need to incorporate various data sources and develop models of the determinants of parenting. For instance, mobile and sensor-based technologies, have been utilised to encourage positive behaviors through monitoring, detection, and (in-situ) feedback [47, 156, 174, 195, 199, 202, 214, 227]. Educational data are being used to predict students at risk and for targeting particular types of intervention strategies [327]. Finally, supervised ML could be used with parent data to automatically predict child mental-health outcomes [75].
Coaching parents during parent-child interactions is an important mechanism of change [313]. A meta-analytic review by Kaminski et al. [182] showed that parent training programs that included coaching produced higher levels of positive parenting behaviors and lower levels of child externalizing problems than programs without those components. Coaching utilises a more non-directive approach in supporting parents behavioural changes [138] treating parents as collaborative partners rather than passive recipients of expert advice [288, 341]. Specifically, the coach encourages the learner’s (parent’s) capacity to engage in self-reflection, thus enabling them to evaluate the effectiveness of their actions and practices [345]. Additionally, this process facilitates the development of a plan for refining and utilising said actions in both present and future scenarios [104, 256].
B GRAPHS
Figure 11: Parent rating of their perceived difficulty in understanding the scenario (Q3)
Footnotes
Supplemental Material
Available for Download
Figure 1: Populated Framework, higher-resolution image
Mapping of scenarios
Full list of features
All scenarios storyboards
Snapshot of process: a. Populated Framework, b. Combinations of cells for scenario building, c. Overview of nine futures. Higher-resolution image.
- Tashia Abry, Chris S. Hulleman, and Sara E. Rimm-Kaufman. 2015. Using Indices of Fidelity to Intervention Core Components to Identify Program Active Ingredients. American Journal of Evaluation 36, 3 (2015), 320–338. https://doi.org/10.1177/1098214014557009Google Scholar
Cross Ref
- Marios Adamou, Grigoris Antoniou, Elissavet Greasidou, Vincenzo Lagani, Paulos Charonyktakis, and Ioannis Tsamardinos. 2018. Mining Free-Text Medical Notes for Suicide Risk Assessment. Proceedings of the 10th Hellenic Conference on Artificial Intelligence (2018), 1–8. https://doi.org/10.1145/3200947.3201020Google ScholarDigital Library
- Naseem Ahmadpour, Sonja Pedell, Angeline Mayasari, and Jeanie Beh. 2019. Co-creating and Assessing Future Wellbeing Technology Using Design Fiction. She Ji: The Journal of Design, Economics, and Innovation 5, 3 (2019), 209–230. https://doi.org/10.1016/j.sheji.2019.08.003Google ScholarCross Ref
- Rodica Ailincai and Annick Weil-Barais. 2013. Parenting Education: Which Intervention Model to Use?Procedia - Social and Behavioral Sciences 106 (2013), 2008–2021. https://doi.org/10.1016/j.sbspro.2013.12.229Google ScholarCross Ref
- Takuto Akiyoshi, Junya Nakanishi, Hiroshi Ishiguro, Hidenobu Sumioka, and Masahiro Shiomi. 2021. A Robot That Encourages Self-Disclosure to Reduce Anger Mood. IEEE Robotics and Automation Letters 6, 4 (2021), 7926–7933. https://doi.org/10.1109/lra.2021.3102326Google ScholarCross Ref
- Tariq Osman Andersen, Francisco Nunes, Lauren Wilcox, Elizabeth Kaziunas, Stina Matthiesen, and Farah Magrabi. 2021. Realizing AI in Healthcare: Challenges Appearing in the Wild. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (2021), 1–5. https://doi.org/10.1145/3411763.3441347Google ScholarDigital Library
- David S. Arnold, Susan G. O’Leary, Lisa S. Wolff, and Maureen M. Acker. 1993. The Parenting Scale: A Measure of Dysfunctional Parenting in Discipline Situations. Psychological Assessment 5, 2 (1993), 137–144. https://doi.org/10.1037/1040-3590.5.2.137Google ScholarCross Ref
- Lindsey Arnold. 2016. Emobie™: A Robot Companion for Children with Anxiety. 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (2016), 413–414. https://doi.org/10.1109/hri.2016.7451782Google ScholarCross Ref
- Dante Arroyo, Yuichi Ishiguro, and Fumihide Tanaka. 2017. Design of a Home Telepresence Robot System for Supporting Childcare. Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (2017), 131–134. https://doi.org/10.1145/3022198.3026337Google ScholarDigital Library
- Kaisa Aunola and Jari-Erik Nurmi. 2005. The Role of Parenting Styles in Children’s Problem Behavior: Parenting Styles in Children’s Behavior. Child Development 76, 6 (2005), 1144–1159. https://doi.org/10.1111/j.1467-8624.2005.00840.x-i1Google ScholarCross Ref
- Kathleen M. Baggett, Betsy Davis, Edward G. Feil, Lisa L. Sheeber, Susan H. Landry, Judith J. Carta, and Craig Leve. 2010. Technologies for Expanding the Reach of Evidence-Based Interventions: Preliminary Results for Promoting Social-Emotional Development in Early Childhood. Topics in Early Childhood Special Education 29, 4 (2010), 226–238. https://doi.org/10.1177/0271121409354782Google ScholarCross Ref
- Sabine Baker, Matthew R. Sanders, and Alina Morawska. 2017. Who Uses Online Parenting Support? A Cross-Sectional Survey Exploring Australian Parents’ Internet Use for Parenting. Journal of Child and Family Studies 26, 3 (2017), 916–927. https://doi.org/10.1007/s10826-016-0608-1Google ScholarCross Ref
- Sabine Baker, Matthew R. Sanders, Karen M.T. Turner, and Alina Morawska. 2017. A randomized controlled trial evaluating a low-intensity interactive online parenting intervention, Triple P Online Brief, with parents of children with early onset conduct problems. Behaviour Research and Therapy 91 (2017), 78–90. https://doi.org/10.1016/j.brat.2017.01.016Google ScholarCross Ref
- Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 2 (1977), 191–215. https://doi.org/10.1037/0033-295x.84.2.191Google ScholarCross Ref
- Albert Bandura. 1978. Social Learning Theory of Aggression. Journal of Communication 28, 3 (1978), 12–29. https://doi.org/10.1111/j.1460-2466.1978.tb01621.xGoogle ScholarCross Ref
- Albert Bandura. 1995. Self-efficacy in Changing Societies. (1995), 1–45. https://doi.org/10.1017/cbo9780511527692.003Google ScholarCross Ref
- Jeffrey Bardzell and Shaowen Bardzell. 2013. What is "critical" about critical design?Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2013), 3297–3306. https://doi.org/10.1145/2470654.2466451Google ScholarDigital Library
- Shaowen Bardzell, Jeffrey Bardzell, Jodi Forlizzi, John Zimmerman, and John Antanitis. 2012. Critical design and critical theory: the challenge of designing for provocation. Proceedings of the Designing Interactive Systems Conference (2012), 288–297. https://doi.org/10.1145/2317956.2318001Google ScholarDigital Library
- Louise Barkhuus, Lionel P Robert, Ingrid Erickson, Adriana S Vivacqua, Lars Rune Christensen, Sandjar Kozubaev, and Carl DiSalvo. 2020. The Future of Public Libraries as Convivial Spaces: A Design Fiction. Companion of the 2020 ACM International Conference on Supporting Group Work (2020), 83–90. https://doi.org/10.1145/3323994.3369901Google ScholarDigital Library
- Jane Barlow, Hanna Bergman, Hege Kornør, Yinghui Wei, and Cathy Bennett. 2016. Group‐based parent training programmes for improving emotional and behavioural adjustment in young children. Cochrane Database of Systematic Reviews8 (2016), CD003680. https://doi.org/10.1002/14651858.cd003680.pub3Google ScholarCross Ref
- Susan M. Barnett and Stephen J. Ceci. 2002. When and Where Do We Apply What We Learn? A Taxonomy for Far Transfer. Psychological Bulletin 128, 4 (2002), 612–637. https://doi.org/10.1037/0033-2909.128.4.612Google ScholarCross Ref
- Luísa BARROS and Klara GREFFIN. 2017. Supporting health-related parenting: A scoping review of programs assisted by the Internet and related technologies. Estudos de Psicologia (Campinas) 34, 03 (2017), 331–344. https://doi.org/10.1590/1982-02752017000300002Google ScholarCross Ref
- Nicole E. Barroso, Lucybel Mendez, Paulo A. Graziano, and Daniel M. Bagner. 2018. Parenting Stress through the Lens of Different Clinical Groups: a Systematic Review & Meta-Analysis. Journal of Abnormal Child Psychology 46, 3 (2018), 449–461. https://doi.org/10.1007/s10802-017-0313-6Google ScholarCross Ref
- Amit Baumel, Aditya Pawar, John M. Kane, and Christoph U. Correll. 2016. Digital Parent Training for Children with Disruptive Behaviors: Systematic Review and Meta-Analysis of Randomized Trials. Journal of Child and Adolescent Psychopharmacology 26, 8 (2016), 740–749. https://doi.org/10.1089/cap.2016.0048Google ScholarCross Ref
- Amit Baumel, Aditya Pawar, John M. Kane, and Christoph U. Correll. 2016. Digital Parent Training for Children with Disruptive Behaviors: Systematic Review and Meta-Analysis of Randomized Trials. Journal of Child and Adolescent Psychopharmacology 26, 8 (2016), 740–749. https://doi.org/10.1089/cap.2016.0048Google ScholarCross Ref
- Diana Baumrind. 1966. Effects of Authoritative Parental Control on Child Behavior. Child Development 37, 4 (1966), 887. https://doi.org/10.2307/1126611Google ScholarCross Ref
- D Baumrind. 1967. Child care practices anteceding three patterns of preschool behavior.Genetic psychology monographs 75, 1 (1967), 43–88.Google Scholar
- Kimberly B Bausback and Eduardo L Bunge. 2021. Meta-Analysis of Parent Training Programs Utilizing Behavior Intervention Technologies. Social Sciences 10, 10 (2021), 367. https://doi.org/10.3390/socsci10100367Google ScholarCross Ref
- Paul Baxter, Emily Ashurst, Robin Read, James Kennedy, and Tony Belpaeme. 2017. Robot education peers in a situated primary school study: Personalisation promotes child learning. PLoS ONE 12, 5 (2017), e0178126. https://doi.org/10.1371/journal.pone.0178126Google ScholarCross Ref
- Harolyn M. E. Belcher, Katara Watkins, Elizabeth Johnson, and Nicholas Ialongo. 2007. Early Head Start: Factors Associated with Caregiver Knowledge of Child Development, Parenting Behavior, and Parenting Stress. NHSA Dialog 10, 1 (2007), 6–19. https://doi.org/10.1080/15240750701301639Google ScholarCross Ref
- Tony Belpaeme, James Kennedy, Aditi Ramachandran, Brian Scassellati, and Fumihide Tanaka. 2018. Social robots for education: A review. Science Robotics 3, 21 (2018). https://doi.org/10.1126/scirobotics.aat5954Google ScholarCross Ref
- J Belsky. 1984. The determinants of parenting: a process model.Child development 55, 1 (1984), 83–96. https://doi.org/10.1111/j.1467-8624.1984.tb00275.xGoogle ScholarCross Ref
- Jay Belsky. 1984. The Determinants of Parenting: A Process Model. Child Development 55, 1 (1984), 83. https://doi.org/10.2307/1129836Google ScholarCross Ref
- J Belsky, B Bell, R H Bradley, N Stallard, and S L Stewart-Brown. 2006. Socioeconomic risk, parenting during the preschool years and child health age 6 years. The European Journal of Public Health 17, 5 (2006), 508–513. https://doi.org/10.1093/eurpub/ckl261Google ScholarCross Ref
- Erin Beneteau, Yini Guan, Olivia K. Richards, Mingrui Ray Zhang, Julie A. Kientz, Jason Yip, and Alexis Hiniker. 2020. Assumptions Checked. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1–23. https://doi.org/10.1145/3380993 This maybe useful for theories of adapting tech.Google ScholarDigital Library
- Fabiane Barreto Vavassori Benitti. 2012. Exploring the educational potential of robotics in schools: A systematic review. Computers & Education 58, 3 (2012), 978–988. https://doi.org/10.1016/j.compedu.2011.10.006Google ScholarDigital Library
- Gary G. Bennett and Russell E. Glasgow. 2009. The Delivery of Public Health Interventions via the Internet: Actualizing Their Potential. Public Health 30, 1 (2009), 273–292. https://doi.org/10.1146/annurev.publhealth.031308.100235Google ScholarCross Ref
- M. Benzeghiba, R. De Mori, O. Deroo, S. Dupont, T. Erbes, D. Jouvet, L. Fissore, P. Laface, A. Mertins, C. Ris, R. Rose, V. Tyagi, and C. Wellekens. 2007. Automatic speech recognition and speech variability: A review. Speech Communication 49, 10-11 (2007), 763–786. https://doi.org/10.1016/j.specom.2007.02.006Google ScholarDigital Library
- Aislinn D. Bergin, Elvira Perez Vallejos, E. Bethan Davies, David Daley, Tamsin Ford, Gordon Harold, Sarah Hetrick, Megan Kidner, Yunfei Long, Sally Merry, Richard Morriss, Kapil Sayal, Edmund Sonuga-Barke, Jo Robinson, John Torous, and Chris Hollis. 2020. Preventive digital mental health interventions for children and young people: a review of the design and reporting of research. NPJ Digital Medicine 3, 1 (2020), 133. https://doi.org/10.1038/s41746-020-00339-7Google ScholarCross Ref
- Cady Berkel, Anne M. Mauricio, Erin Schoenfelder, and Irwin N. Sandler. 2011. Putting the Pieces Together: An Integrated Model of Program Implementation. Prevention Science 12, 1 (2011), 23–33. https://doi.org/10.1007/s11121-010-0186-1Google ScholarCross Ref
- M E Bernal, M D Klinnert, and L A Schultz. 1980. Outcome evaluation of behavioral parent training and client-centered parent counseling for children with conduct problems.Journal of Applied Behavior Analysis 13, 4 (1980), 677–691. https://doi.org/10.1901/jaba.1980.13-677Google ScholarCross Ref
- Ferran Altarriba Bertran, Laura Bisbe Armengol, Cameron Cooke, Ivy Chen, Victor Dong, Binaisha Dastoor, Kelsea Tadano, Fyez Dean, Jessalyn Wang, Adrià Altarriba Bertran, Jared Duval, and Katherine Isbister. 2022. Co-Imagining the Future of Playable Cities. CHI Conference on Human Factors in Computing Systems (2022), 1–19. https://doi.org/10.1145/3491102.3501860Google ScholarDigital Library
- Cindy L. Bethel, Zachary Henkel, Kristen Stives, David C. May, Deborah K. Eakin, Melinda Pilkinton, Alexis Jones, and Megan Stubbs-Richardson. 2016. Using Robots to Interview Children About Bullying: Lessons Learned from an Exploratory Study. 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (2016), 712–717. https://doi.org/10.1109/roman.2016.7745197Google ScholarDigital Library
- A. Bifulco, P.M. Moran, C. Ball, C. Jacobs, R. Baines, A. Bunn, and J. Cavagin. 2002. Childhood adversity, parental vulnerability and disorder: examining inter‐generational transmission of risk. Journal of Child Psychology and Psychiatry 43, 8 (2002), 1075–1086. https://doi.org/10.1111/1469-7610.00234Google ScholarCross Ref
- Jomara Binda, Chien Wen Yuan, Natalie Cope, Hyehyun Park, Eun Kyoung Choe, and John M Carroll. 2018. Supporting Effective Sharing of Health Information among Intergenerational Family Members. Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare (2018), 148–157. https://doi.org/10.1145/3240925.3240936Google ScholarDigital Library
- Scott R. Bishop, Mark Lau, Shauna Shapiro, Linda Carlson, Nicole D. Anderson, James Carmody, Zindel V. Segal, Susan Abbey, Michael Speca, Drew Velting, and Gerald Devins. 2004. Mindfulness: A Proposed Operational Definition. Clinical Psychology: Science and Practice 11, 3 (2004), 230–241. https://doi.org/10.1093/clipsy.bph077Google ScholarCross Ref
- Susanne Biundo and Andreas Wendemuth. 2016. Companion-Technology for Cognitive Technical Systems. KI - Künstliche Intelligenz 30, 1 (2016), 71–75. https://doi.org/10.1007/s13218-015-0414-8Google ScholarCross Ref
- Staffan Björk, Eva Eriksson, Morten Fjeld, Susanne Bødker, Wolmet Barendregt, Mohammad Obaid, Britta F Schulte, Paul Marshall, and Anna L Cox. 2016. Homes For Life. Proceedings of the 9th Nordic Conference on Human-Computer Interaction (2016), 1–10. https://doi.org/10.1145/2971485.2993925Google ScholarDigital Library
- Ann Blandford, Dominic Furniss, and Stephann Makri. 2016. Qualitative HCI Research: Going Behind the Scenes. Synthesis Lectures on Human-Centered Informatics 9, 1 (2016), 1–115. https://doi.org/10.2200/s00706ed1v01y201602hci034Google ScholarCross Ref
- Julian Bleecker. 2009. Design Fiction: A short essay on design, science, fact and fiction.Near Future Laboratory (2009).Google Scholar
- Mark Blythe. 2014. Research through design fiction. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2014), 703–712. https://doi.org/10.1145/2556288.2557098Google ScholarDigital Library
- Mark Blythe and Enrique Encinas. 2018. Research Fiction and Thought Experiments in Design. Foundations and Trends® Human–Computer Interaction 12, 1 (2018), 1–105. https://doi.org/10.1561/1100000070Google ScholarDigital Library
- Guy Bodenmann, Thomas N. Bradbury, and Sandrine Pihet. 2008. Relative Contributions of Treatment-Related Changes in Communication Skills and Dyadic Coping Skills to the Longitudinal Course of Marriage in the Framework of Marital Distress Prevention. Journal of Divorce & Remarriage 50, 1 (2008), 1–21. https://doi.org/10.1080/10502550802365391Google ScholarCross Ref
- Anton Bogdanovych, Deborah Richards, Simeon Simoff, Catherine Pelachaud, Dirk Heylen, Tomas Trescak, Martin H Luerssen, and Tim Hawke. 2018. Virtual Agents as a Service: Applications in Healthcare. Proceedings of the 18th International Conference on Intelligent Virtual Agents (2018), 107–112. https://doi.org/10.1145/3267851.3267858Google ScholarDigital Library
- George A. Bonanno. 2012. Uses and abuses of the resilience construct: Loss, trauma, and health-related adversities. Social Science & Medicine 74, 5 (2012), 753–756. https://doi.org/10.1016/j.socscimed.2011.11.022Google ScholarCross Ref
- Lynne A. Bond and Catherine E. Burns. 2006. Mothers’ Beliefs about Knowledge, Child Development, and Parenting Strategies: Expanding the Goals of Parenting Programs. Journal of Primary Prevention 27, 6 (2006), 555–571. https://doi.org/10.1007/s10935-006-0061-9Google ScholarCross Ref
- Susan M. Breitenstein, Deborah Gross, and Rebecca Christophersen. 2014. Digital Delivery Methods of Parenting Training Interventions: A Systematic Review. Worldviews on Evidence‐Based Nursing 11, 3 (2014), 168–176. https://doi.org/10.1111/wvn.12040Google ScholarCross Ref
- Susan M. Breitenstein, James Shane, Wrenetha Julion, and Deborah Gross. 2015. Developing the eCPP: Adapting an Evidence‐Based Parent Training Program for Digital Delivery in Primary Care Settings. Worldviews on Evidence‐Based Nursing 12, 1 (2015), 31–40. https://doi.org/10.1111/wvn.12074Google ScholarCross Ref
- Svenja Breuer, Maximilian Braun, Daniel Tigard, Alena Buyx, and Ruth Müller. 2023. How Engineers’ Imaginaries of Healthcare Shape Design and User Engagement: A Case Study of a Robotics Initiative for Geriatric Healthcare AI Applications. ACM Transactions on Computer-Human Interaction 30, 2 (2023), 1–33. https://doi.org/10.1145/3577010Google ScholarDigital Library
- Oğuz ’Oz’ Buruk, Oğuzhan Özcan, Gökçe Elif Baykal, Tilbe Göksun, Selçuk Acar, Güler Akduman, Mehmet Aydın Baytaş, Ceylan Beşevli, Joe Best, Aykut Coşkun, Hüseyin Uğur Genç, A Baki Kocaballi, Samuli Laato, Cássia Mota, Konstantinos Papangelis, Marigo Raftopoulos, Richard Ramchurn, Juan Sádaba, Mattia Thibault, Annika Wolff, and Mert Yildiz. 2020. Children in 2077: Designing Children’s Technologies in the Age of Transhumanism. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (2020), 1–14. https://doi.org/10.1145/3334480.3381821Google ScholarDigital Library
- Tracey Bywater, Judy Hutchings, David Daley, Chris Whitaker, Seow Tien Yeo, Karen Jones, Catrin Eames, and Rhiannon Tudor Edwards. 2009. Long-term effectiveness of a parenting intervention for children at risk of developing conduct disorder. British Journal of Psychiatry 195, 4 (2009), 318–324. https://doi.org/10.1192/bjp.bp.108.056531Google ScholarCross Ref
- Caterina Bérubé, Theresa Schachner, Roman Keller, Elgar Fleisch, Florian v Wangenheim, Filipe Barata, and Tobias Kowatsch. 2021. Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review. Journal of Medical Internet Research 23, 3 (2021), e25933. https://doi.org/10.2196/25933Google ScholarCross Ref
- Philippa H. Campbell and L. Brook Sawyer. 2007. Supporting Learning Opportunities in Natural Settings Through Participation-Based Services. Journal of Early Intervention 29, 4 (2007), 287–305. https://doi.org/10.1177/105381510702900402Google ScholarCross Ref
- Susan B. Campbell. 1995. Behavior Problems in Preschool Children: A Review of Recent Research. Journal of Child Psychology and Psychiatry 36, 1 (1995), 113–149. https://doi.org/10.1111/j.1469-7610.1995.tb01657.xGoogle ScholarCross Ref
- Stuart Candy and Jake Dunagan. 2017. Designing an experiential scenario: The People Who Vanished. Futures 86 (2017), 136–153. https://doi.org/10.1016/j.futures.2016.05.006Google ScholarCross Ref
- Ana Catarina Canário, Sonia Byrne, Nicole Creasey, Eliška Kodyšová, Burcu Kömürcü Akik, Aleksandra Lewandowska-Walter, Koraljka Modić Stanke, Ninoslava Pećnik, and Patty Leijten. 2022. The Use of Information and Communication Technologies in Family Support across Europe: A Narrative Review. International Journal of Environmental Research and Public Health 19, 3 (2022), 1488. https://doi.org/10.3390/ijerph19031488Google ScholarCross Ref
- Stephanie M. Carlson, Dorothy J. Mandell, and Luke Williams. 2004. Executive Function and Theory of Mind: Stability and Prediction From Ages 2 to 3. Developmental Psychology 40, 6 (2004), 1105–1122. https://doi.org/10.1037/0012-1649.40.6.1105Google ScholarCross Ref
- Annalise Caron, Bahr Weiss, Vicki Harris, and Tom Catron. 2006. Parenting Behavior Dimensions and Child Psychopathology: Specificity, Task Dependency, and Interactive Relations. Journal of Clinical Child & Adolescent Psychology 35, 1 (2006), 34–45. https://doi.org/10.1207/s15374424jccp3501_4Google ScholarCross Ref
- Bernie Carter. 2007. Parenting: a glut of information. Journal of Child Health Care 11, 2 (2007), 82–84. https://doi.org/10.1177/1367493507079621Google ScholarCross Ref
- Ginevra Castellano, Iolanda Leite, André Pereira, Carlos Martinho, Ana Paiva, and Peter W. McOwan. 2010. Affect recognition for interactive companions: challenges and design in real world scenarios. Journal on Multimodal User Interfaces 3, 1-2 (2010), 89–98. https://doi.org/10.1007/s12193-009-0033-5Google ScholarCross Ref
- Albert Causo, Giang Truong Vo, I-Ming Chen, and Song Huat Yeo. 2015. Robotics and Mechatronics, Proceedings of the 4th IFToMM International Symposium on Robotics and Mechatronics. Mechanisms and Machine Science (2015), 75–84. https://doi.org/10.1007/978-3-319-22368-1_8Google ScholarCross Ref
- Angie Y. Chai, Chun Zhang, and Marilyn Bisberg. 2006. Rethinking Natural Environment Practice: Implications from Examining Various Interpretations and Approaches. Early Childhood Education Journal 34, 3 (2006), 203–208. https://doi.org/10.1007/s10643-006-0115-xGoogle ScholarCross Ref
- Douglas W Challener, Larry J Prokop, and Omar Abu-Saleh. 2019. The Proliferation of Reports on Clinical Scoring Systems: Issues About Uptake and Clinical Utility.JAMA 321, 24 (2019), 2405–2406. https://doi.org/10.1001/jama.2019.5284Google ScholarCross Ref
- Meng-Ying Chan, Yi-Hsuan Lin, Long-Fei Lin, Ting-Wei Lin, Wei-Che Hsu, Chia-yu Chang, Rui Liu, Ko-Yu Chang, Min-hua Lin, and Jane Yung-jen Hsu. 2017. WAKEY. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (2017), 2287–2299. https://doi.org/10.1145/2998181.2998233Google ScholarDigital Library
- Adam M. Chekroud, Julia Bondar, Jaime Delgadillo, Gavin Doherty, Akash Wasil, Marjolein Fokkema, Zachary Cohen, Danielle Belgrave, Robert DeRubeis, Raquel Iniesta, Dominic Dwyer, and Karmel Choi. 2021. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 20, 2 (2021), 154–170. https://doi.org/10.1002/wps.20882Google ScholarCross Ref
- Huili Chen, Anastasia K. Ostrowski, Soo Jung Jang, Cynthia Breazeal, and Hae Won Park. 2022. Designing Long-term Parent-child-robot Triadic Interaction at Home through Lived Technology Experiences and Interviews. 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 00 (2022), 401–408. https://doi.org/10.1109/ro-man53752.2022.9900834Google ScholarDigital Library
- Huili Chen, Hae Won Park, and Cynthia Breazeal. 2020. Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children’s learning and emotive engagement. Computers & Education 150 (2020), 103836. https://doi.org/10.1016/j.compedu.2020.103836Google ScholarDigital Library
- Xuetong Chen, Martin D Sykora, Thomas W Jackson, and Suzanne Elayan. 2018. What about Mood Swings: Identifying Depression on Twitter with Temporal Measures of Emotions. Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18 (2018), 1653–1660. https://doi.org/10.1145/3184558.3191624Google ScholarDigital Library
- Zhenyu Chen, Mu Lin, Fanglin Chen, Nicholas D. Lane, Giuseppe Cardone, Rui Wang, Tianxing Li, Yiqiang Chen, Tanzeem Choudhury, and Andrew T. Campbell. 2013. Unobtrusive sleep monitoring using smartphones. Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques (2013), 145–152. https://doi.org/10.4108/icst.pervasivehealth.2013.252148Google ScholarDigital Library
- Helen Cheng, Daniel Hayes, Julian Edbrooke‐Childs, Kate Martin, Louise Chapman, and Miranda Wolpert. 2017. What approaches for promoting shared decision‐making are used in child mental health? A scoping review. Clinical Psychology & Psychotherapy 24, 6 (2017), O1495–O1511. https://doi.org/10.1002/cpp.2106Google ScholarCross Ref
- Riccardo Chianella, Marco Mandolfo, Riccardo Lolatto, and Margherita Pillan. 2021. Human-Computer Interaction. Interaction Techniques and Novel Applications, Thematic Area, HCI 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part II. Lecture Notes in Computer Science (2021), 357–371. https://doi.org/10.1007/978-3-030-78465-2_27Google ScholarDigital Library
- Dana C Childress. 2004. Special Instruction and Natural Environments: Best Practices in Early Intervention. Infants & Young Children 17, 2 (2004), 162–170. https://doi.org/10.1097/00001163-200404000-00007Google ScholarCross Ref
- Kai-Yi Chin, Zeng-Wei Hong, and Yen-Lin Chen. 2014. Impact of Using an Educational Robot-Based Learning System on Students’ Motivation in Elementary Education. IEEE Transactions on Learning Technologies 7, 4 (2014), 333–345. https://doi.org/10.1109/tlt.2014.2346756Google ScholarCross Ref
- Bruce E. Compas and Rebecca A. Williams. 1990. Stress, coping, and adjustment in mothers and young adolescents in single‐ and two‐parent families. American Journal of Community Psychology 18, 4 (1990), 525–545. https://doi.org/10.1007/bf00938058Google ScholarCross Ref
- Alistair Cooper and Sheila Redfern. 2015. Reflective Parenting, A guide to understanding what’s going on in your child’s mind. (2015). https://doi.org/10.4324/9781315764108Google ScholarCross Ref
- Linda K. Cordisco, Phillip S. Strain, and Nancy Depew. 1988. Assessment for Generalization of Parenting Skills in Home Settings. Research and Practice for Persons with Severe Disabilities 13, 3 (1988), 202–210. https://doi.org/10.1177/154079698801300311Google ScholarCross Ref
- Samantha M. Corralejo and Melanie M. Domenech Rodríguez. 2018. Technology in Parenting Programs: A Systematic Review of Existing Interventions. Journal of Child and Family Studies 27, 9 (2018), 2717–2731. https://doi.org/10.1007/s10826-018-1117-1Google ScholarCross Ref
- Dan Cosley, Andrea Forte, Luigina Ciolfi, David McDonald, Christopher L Schaefbauer, Danish U Khan, Amy Le, Garrett Sczechowski, and Katie A Siek. 2015. Snack Buddy. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (2015), 1045–1057. https://doi.org/10.1145/2675133.2675180Google ScholarDigital Library
- E. Jane Costello, Helen L. Egger, William Copeland, Alaattin Erkanli, and Adrian Angold. 2011. Anxiety Disorders in Children and Adolescents. (2011), 56–75. https://doi.org/10.1017/cbo9780511994920.004Google ScholarCross Ref
- Paul Coulton, Joseph Galen Lindley, Miriam Sturdee, and Michael Stead. 2017. Design Fiction as World Building. https://core.ac.uk/download/pdf/76962562.pdfGoogle Scholar
- Philip A. Cowan and Carolyn Pape Cowan. 2014. Controversies in Couple Relationship Education (CRE): Overlooked Evidence and Implications for Research and Policy. Psychology, Public Policy, and Law 20, 4 (2014), 361–383. https://doi.org/10.1037/law0000025Google ScholarCross Ref
- David Coyle, Conor Linehan, Karen Tang, and Sian Lindley. 2012. Interaction design and emotional wellbeing. CHI ’12 Extended Abstracts on Human Factors in Computing Systems (2012), 2775–2778. https://doi.org/10.1145/2212776.2212718Google ScholarDigital Library
- Shaniece Criss, Jennifer A. Woo Baidal, Roberta E. Goldman, Meghan Perkins, Courtney Cunningham, and Elsie M. Taveras. 2015. The Role of Health Information Sources in Decision-Making Among Hispanic Mothers During Their Children’s First 1000 Days of Life. Maternal and Child Health Journal 19, 11 (2015), 2536–2543. https://doi.org/10.1007/s10995-015-1774-2Google ScholarCross Ref
- Keith A Crnic and Cathryn L Booth. 1991. Mothers’ and Fathers’ Perceptions of Daily Hassles of Parenting across Early Childhood. Journal of Marriage and the Family 53, 4 (1991), 1042. https://doi.org/10.2307/353007Google ScholarCross Ref
- E. Mark Cummings and Patrick T. Davies. 2002. Effects of marital conflict on children: recent advances and emerging themes in process‐oriented research. Journal of Child Psychology and Psychiatry 43, 1 (2002), 31–63. https://doi.org/10.1111/1469-7610.00003Google ScholarCross Ref
- E. Mark Cummings, W. Brad Faircloth, Patricia M. Mitchell, Jennifer S. Cummings, and Alice C. Schermerhorn. 2008. Evaluating a Brief Prevention Program for Improving Marital Conflict in Community Families. Journal of Family Psychology 22, 2 (2008), 193–202. https://doi.org/10.1037/0893-3200.22.2.193Google ScholarCross Ref
- E. Mark Cummings, Marcie C. Goeke-Morey, and Lauren M. Papp. 2004. Everyday Marital Conflict and Child Aggression. Journal of Abnormal Child Psychology 32, 2 (2004), 191–202. https://doi.org/10.1023/b:jacp.0000019770.13216.beGoogle ScholarCross Ref
- E. Mark Cummings, Marcie C. Goeke‐morey, and Lauren M. Papp. 2003. Children’s Responses to Everyday Marital Conflict Tactics in the Home. Child Development 74, 6 (2003), 1918–1929. https://doi.org/10.1046/j.1467-8624.2003.00646.xGoogle ScholarCross Ref
- Scott Davidoff, Min Kyung Lee, Anind K. Dey, and John Zimmerman. 2007. UbiComp 2007: Ubiquitous Computing, 9th International Conference, UbiComp 2007, Innsbruck, Austria, September 16-19, 2007. Proceedings. Lecture Notes in Computer Science (2007), 429–446. https://doi.org/10.1007/978-3-540-74853-3_25Google ScholarCross Ref
- Scott Davidoff, John Zimmerman, and Anind K Dey. 2010. How routine learners can support family coordination. Proceedings of the 28th international conference on Human factors in computing systems - CHI ’10 (2010), 2461–2470. https://doi.org/10.1145/1753326.1753699Google ScholarDigital Library
- Maayan Davidov and Joan E. Grusec. 2006. Untangling the Links of Parental Responsiveness to Distress and Warmth to Child Outcomes. Child Development 77, 1 (2006), 44–58. https://doi.org/10.1111/j.1467-8624.2006.00855.xGoogle ScholarCross Ref
- Deborah Winders Davis, M. Cynthia Logsdon, Krista Vogt, Jeff Rushton, John Myers, Adrian Lauf, and Felicia Hogan. 2017. Parent Education is Changing. MCN, The American Journal of Maternal/Child Nursing 42, 5 (2017), 248–256. https://doi.org/10.1097/nmc.0000000000000353Google ScholarCross Ref
- Jamin J. Day and Matthew R. Sanders. 2017. Mediators of Parenting Change Within a Web-Based Parenting Program: Evidence From a Randomized Controlled Trial of Triple P Online. Couple and Family Psychology: Research and Practice 6, 3 (2017), 154–170. https://doi.org/10.1037/cfp0000083Google ScholarCross Ref
- Jamin J. Day and Matthew R. Sanders. 2018. Do Parents Benefit From Help When Completing a Self-Guided Parenting Program Online? A Randomized Controlled Trial Comparing Triple P Online With and Without Telephone Support. Behavior Therapy 49, 6 (2018), 1020–1038. https://doi.org/10.1016/j.beth.2018.03.002Google ScholarCross Ref
- Kirby Deater‐Deckard, Zhe Wang, Nan Chen, and Martha Ann Bell. 2012. Maternal executive function, harsh parenting, and child conduct problems. Journal of Child Psychology and Psychiatry 53, 10 (2012), 1084–1091. https://doi.org/10.1111/j.1469-7610.2012.02582.xGoogle ScholarCross Ref
- Orianna DeMasi and Benjamin Recht. 2017. A step towards quantifying when an algorithm can and cannot predict an individual’s wellbeing. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (2017), 763–771. https://doi.org/10.1145/3123024.3125609Google ScholarDigital Library
- James A. Dilling, Stephen J. Swensen, Michele R. Hoover, Gene C. Dankbar, Amerett L. Donahoe-Anshus, M. Hassan Murad, and Jeff T. Mueller. 2013. Accelerating the Use of Best Practices: The Mayo Clinic Model of Diffusion. The Joint Commission Journal on Quality and Patient Safety 39, 4 (2013), 167–AP2. https://doi.org/10.1016/s1553-7250(13)39023-0Google ScholarCross Ref
- Cassandra K. Dittman, Susan P. Farruggia, Melanie L. Palmer, Matthew R. Sanders, and Louise J. Keown. 2014. Predicting Success in an Online Parenting Intervention: The Role of Child, Parent, and Family Factors. Journal of Family Psychology 28, 2 (2014), 236–243. https://doi.org/10.1037/a0035991Google ScholarCross Ref
- Stefania Druga, Fee Lia Christoph, and Amy J Ko. 2022. Family as a Third Space for AI Literacies: How do children and parents learn about AI together?CHI Conference on Human Factors in Computing Systems (2022), 1–17. https://doi.org/10.1145/3491102.3502031Google ScholarDigital Library
- Yao Du, Kerri Zhang, Sruthi Ramabadran, and Yusa Liu. 2021. “Alexa, What is That Sound?” A Video Analysis of Child-Agent Communication From Two Amazon Alexa Games. Interaction Design and Children (2021), 513–520. https://doi.org/10.1145/3459990.3465195Google ScholarDigital Library
- Eric F. Dubow and Maria F. Ippolito. 1994. Effects of poverty and quality of the home environment on changes in the academic and behavioral adjustment of elementary school-age children. Journal of Clinical Child Psychology 23, 4 (1994), 401–412. https://doi.org/10.1207/s15374424jccp2304_6Google ScholarCross Ref
- Jean E. Dumas, Jenelle Nissley-Tsiopinis, and Angela D. Moreland. 2007. From Intent to Enrollment, Attendance, and Participation in Preventive Parenting Groups. Journal of Child and Family Studies 16, 1 (2007), 1–26. https://doi.org/10.1007/s10826-006-9042-0Google ScholarCross Ref
- Greg J. Duncan, Jeanne Brooks‐Gunn, and Pamela Kato Klebanov. 1994. Economic Deprivation and Early Childhood Development. Child Development 65, 2 (1994), 296–318. https://doi.org/10.1111/j.1467-8624.1994.tb00752.xGoogle ScholarCross Ref
- Anthony Dunne and Fiona Raby. 2013. Speculative Everything : Design, Fiction, and Social Dreaming. The MIT Press. https://readings.design/PDF/speculative-everything.pdfGoogle Scholar
- Sylmarie Dvila-Montero, Jocelyn Alisa Dana-L, Gary Bente, Angela T. Hall, and Andrew J. Mason. 2020. Review and Challenges of Technologies for Real-Time Human Behavior Monitoring. IEEE Transactions on Biomedical Circuits and Systems 15, 1 (2020), 2–28. https://doi.org/10.1109/tbcas.2021.3060617Google ScholarCross Ref
- Judith Dörrenbächer, Ronda Ringfort-Felner, and Marc Hassenzahl. 2023. The Intricacies of Social Robots: Secondary Analysis of Fictional Documentaries to Explore the Benefits and Challenges of Robots in Complex Social Settings. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (2023), 1–13. https://doi.org/10.1145/3544548.3581526Google ScholarDigital Library
- Chloe Eghtebas, Gudrun Klinker, Susanne Boll, and Marion Koelle. 2023. Co-Speculating on Dark Scenarios and Unintended Consequences of a Ubiquitous(ly) Augmented Reality. Proceedings of the 2023 ACM Designing Interactive Systems Conference (2023), 2392–2407. https://doi.org/10.1145/3563657.3596073Google ScholarDigital Library
- Nancy Eisenberg, Ivanna K Guthrie, Richard A Fabes, Mark Reiser, Bridget C Murphy, Robin Holgren, Pat Maszk, and Sandra Losoya. 1997. The Relations of Regulation and Emotionality to Resiliency and Competent Social Functioning in Elementary School Children. Child Development 68, 2 (1997), 295. https://doi.org/10.2307/1131851Google ScholarCross Ref
- Stephen N Elliott and R T Busse. 1991. Social Skills Assessment and Intervention with Children and Adolescents: Guidelines for Assessment and Training Procedures. School Psychology International 12, 1-2 (1991), 63–83. https://doi.org/10.1177/0143034391121006Google ScholarCross Ref
- Edona Elshan, Naim Zierau, Christian Engel, Andreas Janson, and Jan Marco Leimeister. 2022. Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future. Information Systems Frontiers 24, 3 (2022), 699–730. https://doi.org/10.1007/s10796-021-10230-9Google ScholarDigital Library
- Sheila M. Eyberg, Melanie M. Nelson, and Stephen R. Boggs. 2008. Evidence-Based Psychosocial Treatments for Children and Adolescents With Disruptive Behavior. Journal of Clinical Child & Adolescent Psychology 37, 1 (2008), 215–237. https://doi.org/10.1080/15374410701820117Google ScholarCross Ref
- Sheila M. Eyberg and Elizabeth A. Robinson. 1982. Parent‐child interaction training: Effects on family functioning. Journal of Clinical Child Psychology 11, 2 (1982), 130–137. https://doi.org/10.1080/15374418209533076Google ScholarCross Ref
- Guy Fagherazzi, Aurélie Fischer, Muhannad Ismael, and Vladimir Despotovic. 2021. Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice. Digital Biomarkers 5, 1 (2021), 78–88. https://doi.org/10.1159/000515346Google ScholarCross Ref
- Jerry Alan Fails, Elham Beheshti, Katya Borgos-Rodriguez, and Anne Marie Piper. 2019. Supporting Parent-Child Collaborative Learning through Haptic Feedback Displays. Proceedings of the 18th ACM International Conference on Interaction Design and Children (2019), 58–70. https://doi.org/10.1145/3311927.3323137Google ScholarDigital Library
- Pedro Gil Farias, Roy Bendor, and Bregje F van Eekelen. 2022. Social dreaming together: A critical exploration of participatory speculative design. Proceedings of the Participatory Design Conference 2022 - Volume 2 (2022), 147–154. https://doi.org/10.1145/3537797.3537826Google ScholarDigital Library
- David P Farrington. 2007. Childhood risk factors and risk-focused prevention. The Oxford handbook of criminology, Vol. 4.Google Scholar
- Edward G. Feil, Peter G. Sprengelmeyer, and Craig Leve. 2018. A Randomized Study of a Mobile Behavioral Parent Training Application. Telemedicine and e-Health 24, 6 (2018), 457–463. https://doi.org/10.1089/tmj.2017.0137Google ScholarCross Ref
- Jasper Feine, Ulrich Gnewuch, Stefan Morana, and Alexander Maedche. 2019. A Taxonomy of Social Cues for Conversational Agents. International Journal of Human-Computer Studies 132 (2019), 138–161. https://doi.org/10.1016/j.ijhcs.2019.07.009Google ScholarDigital Library
- Chaonan Feng, Huimin Gao, Xuefeng B. Ling, Jun Ji, and Yantao Ma. 2018. Shorten Bipolarity Checklist for the Differentiation of Subtypes of Bipolar Disorder Using Machine Learning. Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology (2018), 162–166. https://doi.org/10.1145/3194480.3194508Google ScholarDigital Library
- Melanie A. Fernandez and Sheila M. Eyberg. 2009. Predicting Treatment and Follow-up Attrition in Parent–Child Interaction Therapy. Journal of Abnormal Child Psychology 37, 3 (2009), 431–441. https://doi.org/10.1007/s10802-008-9281-1Google ScholarCross Ref
- Barbara H. Fiese and Ross D. Parke. 2002. Introduction to the Special Section on Family Routines and Rituals. Journal of Family Psychology 16, 4 (2002), 379–380. https://doi.org/10.1037/0893-3200.16.4.379Google ScholarCross Ref
- Frank D. Fincham. 1998. Child Development and Marital Relations. Child Development 69, 2 (1998), 543–574. https://doi.org/10.1111/j.1467-8624.1998.tb06207.xGoogle ScholarCross Ref
- Julia Fink, Séverin Lemaignan, Pierre Dillenbourg, Philippe Rétornaz, Florian Vaussard, Alain Berthoud, Francesco Mondada, Florian Wille, and Karmen Franinović. 2014. Which robot behavior can motivate children to tidy up their toys?: design and evaluation of "ranger". Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction (2014), 439–446. https://doi.org/10.1145/2559636.2559659Google ScholarDigital Library
- Kathleen Kara Fitzpatrick, Alison Darcy, and Molly Vierhile. 2017. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR Mental Health 4, 2 (2017), e19. https://doi.org/10.2196/mental.7785Google ScholarCross Ref
- Juan M. Flujas-Contreras, Azucena García-Palacios, and Inmaculada Gómez. 2019. Technology-based parenting interventions for children’s physical and psychological health: a systematic review and meta-analysis. Psychological Medicine 49, 11 (2019), 1787–1798. https://doi.org/10.1017/s0033291719000692Google ScholarCross Ref
- Peter Fonagy, Miriam Steele, Howard Steele, George S. Moran, and Anna C. Higgitt. 1991. The capacity for understanding mental states: The reflective self in parent and child and its significance for security of attachment. Infant Mental Health Journal 12, 3 (1991), 201–218. https://doi.org/10.1002/1097-0355(199123)12:3<201::aid-imhj2280120307>3.0.co;2-7Google ScholarCross Ref
- Terrence Fong, Illah Nourbakhsh, and Kerstin Dautenhahn. 2003. A survey of socially interactive robots. Robotics and Autonomous Systems 42, 3-4 (2003), 143–166. https://doi.org/10.1016/s0921-8890(02)00372-xGoogle ScholarCross Ref
- Lise Fox, Mary Louise Hemmeter, Patricia Snyder, Denise Perez Binder, and Shelley Clarke. 2011. Coaching Early Childhood Special Educators to Implement a Comprehensive Model for Promoting Young Children’s Social Competence. Topics in Early Childhood Special Education 31, 3 (2011), 178–192. https://doi.org/10.1177/0271121411404440Google ScholarCross Ref
- Ara Francis. 2012. Stigma in an era of medicalisation and anxious parenting: how proximity and culpability shape middle‐class parents’ experiences of disgrace. Sociology of Health & Illness 34, 6 (2012), 927–942. https://doi.org/10.1111/j.1467-9566.2011.01445.xGoogle ScholarCross Ref
- Mairead Furlong, Sinead McGilloway, Tracey Bywater, Judy Hutchings, Susan M. Smith, and Michael Donnelly. 2012. Behavioural and cognitive‐behavioural group‐based parenting programmes for early‐onset conduct problems in children aged 3 to 12 years. Campbell Systematic Reviews 8, 1 (2012), 1–239. https://doi.org/10.4073/csr.2012.12Google ScholarCross Ref
- Susan Fussell, Wayne Lutters, Meredith Ringel Morris, Madhu Reddy, Inseok Hwang, Chungkuk Yoo, Chanyou Hwang, Dongsun Yim, Youngki Lee, Chulhong Min, John Kim, and Junehwa Song. 2014. TalkBetter. Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (2014), 1283–1296. https://doi.org/10.1145/2531602.2531668Google ScholarDigital Library
- Silvia Gabrielli, Silvia Rizzi, Sara Carbone, and Valeria Donisi. 2020. A Chatbot-Based Coaching Intervention for Adolescents to Promote Life Skills: Pilot Study. JMIR Human Factors 7, 1 (2020), e16762. https://doi.org/10.2196/16762Google ScholarCross Ref
- Radhika Garg and Subhasree Sengupta. 2020. He Is Just Like Me. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1–24. https://doi.org/10.1145/3381002Google ScholarDigital Library
- Dedre Gentner, Jeffrey Loewenstein, and Leigh Thompson. 2003. Learning and Transfer: A General Role for Analogical Encoding. Journal of Educational Psychology 95, 2 (2003), 393–408. https://doi.org/10.1037/0022-0663.95.2.393Google ScholarCross Ref
- Abigail H. Gewirtz, Susanne S. Lee, Gerald J. August, and Yaliu He. 2019. Does Giving Parents Their Choice of Interventions for Child Behavior Problems Improve Child Outcomes?Prevention Science 20, 1 (2019), 78–88. https://doi.org/10.1007/s11121-018-0865-xGoogle ScholarCross Ref
- Michail N Giannakos, Letizia Jaccheri, Monica Divitini, Ofir Sadka, Hadas Erel, Andrey Grishko, and Oren Zuckerman. 2018. Tangible interaction in parent-child collaboration: encouraging awareness and reflection. Proceedings of the 17th ACM Conference on Interaction Design and Children (2018), 157–169. https://doi.org/10.1145/3202185.3202746Google ScholarDigital Library
- Karen Glanz, Frances Marcus Lewis, and Barbara K. Rimer. 1991. Health Behavior and Health Education: Theory, Research, and Practice.Medicine & Science in Sports & Exercise 23, 12 (1991), 1404. https://doi.org/10.1249/00005768-199112000-00016Google ScholarCross Ref
- Sherryl H. Goodman, Bill Barfoot, Alice A. Frye, and Andrea M. Belli. 1999. Dimensions of Marital Conflict and Children’s Social Problem-Solving Skills. Journal of Family Psychology 13, 1 (1999), 33–45. https://doi.org/10.1037/0893-3200.13.1.33Google ScholarCross Ref
- Jacqueline J Goodnow. 1988. Parents’ Ideas, Actions, and Feelings: Models and Methods from Developmental and Social Psychology. Child Development 59, 2 (1988), 286. https://doi.org/10.2307/1130312Google ScholarCross Ref
- Eberhard Graether and Florian Mueller. 2012. Joggobot: a flying robot as jogging companion. CHI ’12 Extended Abstracts on Human Factors in Computing Systems (2012), 1063–1066. https://doi.org/10.1145/2212776.2212386Google ScholarDigital Library
- Georgia M Green. 2012. Pragmatics and Natural Language Understanding. (2012). https://doi.org/10.4324/9780203053546Google ScholarCross Ref
- Trisha Greenhalgh, Joseph Wherton, Chrysanthi Papoutsi, Jennifer Lynch, Gemma Hughes, Christine A’Court, Susan Hinder, Nick Fahy, Rob Procter, and Sara Shaw. 2017. Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. Journal of Medical Internet Research 19, 11 (2017), e367. https://doi.org/10.2196/jmir.8775Google ScholarCross Ref
- Deborah Gross, Tricia Johnson, Alison Ridge, Christine Garvey, Wrenetha Julion, Anne Brusius Treysman, Susan Breitenstein, and Louis Fogg. 2011. Cost-Effectiveness of Childcare Discounts on Parent Participation in Preventive Parent Training in Low-Income Communities. The Journal of Primary Prevention 32, 5-6 (2011), 283–298. https://doi.org/10.1007/s10935-011-0255-7Google ScholarCross Ref
- Horst-Michael Gross, Andrea Scheidig, Steffen Müller, Benjamin Schütz, Christa Fricke, and Sibylle Meyer. 2019. Living with a Mobile Companion Robot in your Own Apartment - Final Implementation and Results of a 20-Weeks Field Study with 20 Seniors*. 2019 International Conference on Robotics and Automation (ICRA) 00 (2019), 2253–2259. https://doi.org/10.1109/icra.2019.8793693Google ScholarDigital Library
- John H. Grych and Frank D. Fincham. 1993. Children’s Appraisals of Marital Conflict: Initial Investigations of the Cognitive‐Contextual Framework. Child Development 64, 1 (1993), 215–230. https://doi.org/10.1111/j.1467-8624.1993.tb02905.xGoogle ScholarCross Ref
- Siwen Guo and Christoph Schommer. 2017. Embedding of the Personalized Sentiment Engine PERSEUS in an Artificial Companion. 2017 International Conference on Companion Technology (ICCT) (2017), 1–3. https://doi.org/10.1109/companion.2017.8287080Google ScholarCross Ref
- Naomi J. Hackworth, Jan Matthews, Elizabeth M. Westrupp, Cattram Nguyen, Tracey Phan, Amanda Scicluna, Warren Cann, Donna Bethelsen, Shannon K. Bennetts, and Jan M. Nicholson. 2018. What Influences Parental Engagement in Early Intervention? Parent, Program and Community Predictors of Enrolment, Retention and Involvement. Prevention Science 19, 7 (2018), 880–893. https://doi.org/10.1007/s11121-018-0897-2Google ScholarCross Ref
- W. Kim Halford and Guy Bodenmann. 2013. Effects of relationship education on maintenance of couple relationship satisfaction. Clinical Psychology Review 33, 4 (2013), 512–525. https://doi.org/10.1016/j.cpr.2013.02.001Google ScholarCross Ref
- Jeonghye Han, Miheon Jo, Sungju Park, and Sungho Kim. 2005. The Educational Use of Home Robots for Children. ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005 (2005), 378–383. https://doi.org/10.1109/roman.2005.1513808Google ScholarCross Ref
- Christina N. Harrington, Shamika Klassen, and Yolanda A. Rankin. 2022. “All that You Touch, You Change”: Expanding the Canon of Speculative Design Towards Black Futuring. CHI Conference on Human Factors in Computing Systems (2022), 1–10. https://doi.org/10.1145/3491102.3502118Google ScholarDigital Library
- Tracy Harwood, Tony Garry, and Russell Belk. 2019. Design fiction diegetic prototyping: a research framework for visualizing service innovations. Journal of Services Marketing 34, 1 (2019), 59–73. https://doi.org/10.1108/jsm-11-2018-0339Google ScholarCross Ref
- Divna M. Haslam, Amelia Tee, and Sabine Baker. 2017. The Use of Social Media as a Mechanism of Social Support in Parents. Journal of Child and Family Studies 26, 7 (2017), 2026–2037. https://doi.org/10.1007/s10826-017-0716-6Google ScholarCross Ref
- Yaliu He, Abigail Gewirtz, Susanne Lee, Nicole Morrell, and Gerald August. 2016. A randomized preference trial to inform personalization of a parent training program implemented in community mental health clinics. Translational Behavioral Medicine 6, 1 (2016), 73–80. https://doi.org/10.1007/s13142-015-0366-4Google ScholarCross Ref
- Yaliu He, Abigail H. Gewirtz, Susanne Lee, and Gerald August. 2018. Do Parent Preferences for Child Conduct Problem Interventions Impact Parenting Outcomes? A Pilot Study in Community Children’s Mental Health Settings. Journal of Marital and Family Therapy 44, 4 (2018), 716–729. https://doi.org/10.1111/jmft.12310Google ScholarCross Ref
- Pamela Hinds, John C Tang, Jian Wang, Jakob Bardram, Nicolas Ducheneaut, Frank Chen, Eric Hekler, Jinhui Hu, Shen Li, and Candy Zhao. 2011. Designing for context-aware health self-monitoring, feedback, and engagement. Proceedings of the ACM 2011 conference on Computer supported cooperative work - CSCW ’11 (2011), 613–616. https://doi.org/10.1145/1958824.1958927Google ScholarDigital Library
- Erika Hoff and Brett Laursen. 2019. Handbook of Parenting. (2019), 421–447. https://doi.org/10.4324/9780429401459-13Google ScholarCross Ref
- Robin M. Hogarth. 2010. Intuition: A Challenge for Psychological Research on Decision Making. Psychological Inquiry 21, 4 (2010), 338–353. https://doi.org/10.1080/1047840x.2010.520260Google ScholarCross Ref
- Kendal Holtrop, Jared A. Durtschi, and Marion S. Forgatch. 2022. Investigating Active Ingredients of the GenerationPMTO Intervention: Predictors of Postintervention Change Trajectories in Parenting Practices. Journal of Family Psychology 36, 2 (2022), 212–224. https://doi.org/10.1037/fam0000925Google ScholarCross Ref
- Stephanie Houde, Vera Liao, Jacquelyn Martino, Michael Muller, David Piorkowski, John Richards, Justin Weisz, and Yunfeng Zhang. 2020. Business (mis)Use Cases of Generative AI. arXiv (2020). https://doi.org/10.48550/arxiv.2003.07679 arXiv:2003.07679Google ScholarCross Ref
- Keng-Yen Huang, Margaret O’Brien Caughy, Janice L. Genevro, and Therese L. Miller. 2005. Maternal knowledge of child development and quality of parenting among White, African-American and Hispanic mothers. Journal of Applied Developmental Psychology 26, 2 (2005), 149–170. https://doi.org/10.1016/j.appdev.2004.12.001Google ScholarCross Ref
- Bernd Huber, Richard F Davis, Allison Cotter, Emily Junkin, Mindy Yard, Stuart Shieber, Elizabeth Brestan-Knight, and Krzysztof Z Gajos. 2019. SpecialTime: Automatically Detecting Dialogue Acts from Speech to Support Parent-Child Interaction Therapy. Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (2019), 139–148. https://doi.org/10.1145/3329189.3329203Google ScholarDigital Library
- J. McV. Hunt and John Paraskevopoulos. 1980. Children’s Psychological Development as a Function of the Inaccuracy of Their Mothers’ Knowledge of Their Abilities. The Journal of Genetic Psychology 136, 2 (1980), 285–298. https://doi.org/10.1080/00221325.1980.10534123Google ScholarCross Ref
- Marinus H van IJzendoorn, Carlo Schuengel, Qiang Wang, and Marian J Bakermans-Kranenburg. 2022. Improving parenting, child attachment, and externalizing behaviors: Meta-analysis of the first 25 randomized controlled trials on the effects of Video-feedback Intervention to promote Positive Parenting and Sensitive Discipline. Development and Psychopathology 35, 1 (2022), 241–256. https://doi.org/10.1017/s0954579421001462Google ScholarCross Ref
- Thomas R. Insel. 2018. Digital phenotyping: a global tool for psychiatry. World Psychiatry 17, 3 (2018), 276–277. https://doi.org/10.1002/wps.20550Google ScholarCross Ref
- Antje Janssen, Jens Passlick, Davinia Rodríguez Cardona, and Michael H. Breitner. 2020. Virtual Assistance in Any Context. Business & Information Systems Engineering 62, 3 (2020), 211–225. https://doi.org/10.1007/s12599-020-00644-1Google ScholarCross Ref
- Deborah J. Jones, Rex Forehand, Jessica Cuellar, Carlye Kincaid, Justin Parent, Nicole Fenton, and Nada Goodrum. 2013. Harnessing innovative technologies to advance children’s mental health: Behavioral parent training as an example. Clinical Psychology Review 33, 2 (2013), 241–252. https://doi.org/10.1016/j.cpr.2012.11.003Google ScholarCross Ref
- Tracy L. Jones and Ronald J. Prinz. 2005. Potential roles of parental self-efficacy in parent and child adjustment: A review. Clinical Psychology Review 25, 3 (2005), 341–363. https://doi.org/10.1016/j.cpr.2004.12.004Google ScholarCross Ref
- Femmie Juffer, Marian J Bakermans-Kranenburg, and Marinus H van IJzendoorn. 2017. Pairing attachment theory and social learning theory in video-feedback intervention to promote positive parenting. Current Opinion in Psychology 15 (2017), 189–194. https://doi.org/10.1016/j.copsyc.2017.03.012Google ScholarCross Ref
- Femmie Juffer and Miriam Steele. 2014. What words cannot say: the telling story of video in attachment-based interventions. Attachment & Human Development 16, 4 (2014), 307–314. https://doi.org/10.1080/14616734.2014.912484Google ScholarCross Ref
- Peter H. Kahn, Heather E. Gary, and Solace Shen. 2013. Children’s Social Relationships With Current and Near‐Future Robots. Child Development Perspectives 7, 1 (2013), 32–37. https://doi.org/10.1111/cdep.12011Google ScholarCross Ref
- Ann P. Kaiser and Terry B. Hancock. 2003. Teaching Parents New Skills to Support Their Young Children’s Development. Infants & Young Children 16, 1 (2003), 9–21. https://doi.org/10.1097/00001163-200301000-00003Google ScholarCross Ref
- Jennifer Wyatt Kaminski, Linda Anne Valle, Jill H. Filene, and Cynthia L. Boyle. 2008. A Meta-analytic Review of Components Associated with Parent Training Program Effectiveness. Journal of Abnormal Child Psychology 36, 4 (2008), 567–589. https://doi.org/10.1007/s10802-007-9201-9Google ScholarCross Ref
- G. A. Kane, V. A. Wood, and J. Barlow. 2007. Parenting programmes: a systematic review and synthesis of qualitative research. Child: Care, Health and Development 33, 6 (2007), 784–793. https://doi.org/10.1111/j.1365-2214.2007.00750.xGoogle ScholarCross Ref
- Paul Karoly. 1993. Mechanisms of Self-Regulation: A Systems View. Annual Review of Psychology 44, 1 (1993), 23–52. https://doi.org/10.1146/annurev.ps.44.020193.000323Google ScholarCross Ref
- Alan E Kazdin. 2015. Evidence-based psychotherapies II: changes in models of treatment and treatment delivery. South African Journal of Psychology 45, 1 (2015), 3–21. https://doi.org/10.1177/0081246314538733Google ScholarCross Ref
- Christina Kelley, Bongshin Lee, and Lauren Wilcox. 2017. Self-tracking for Mental Wellness: Understanding Expert Perspectives and Student Experiences. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems 2017 (2017), 629–641. https://doi.org/10.1145/3025453.3025750Google ScholarDigital Library
- Louise J. Keown, Matthew R. Sanders, Nike Franke, and Matthew Shepherd. 2018. Te Whānau Pou Toru: a Randomized Controlled Trial (RCT) of a Culturally Adapted Low-Intensity Variant of the Triple P-Positive Parenting Program for Indigenous Māori Families in New Zealand. Prevention Science 19, 7 (2018), 954–965. https://doi.org/10.1007/s11121-018-0886-5Google ScholarCross Ref
- Wonjung Kim, Seungchul Lee, Seonghoon Kim, Sungbin Jo, Chungkuk Yoo, Inseok Hwang, Seungwoo Kang, and Junehwa Song. 2020. Dyadic Mirror. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 3 (2020), 1–29. https://doi.org/10.1145/3411815Google ScholarDigital Library
- Yanghee Kim and Amy L. Baylor. 2016. Research-Based Design of Pedagogical Agent Roles: a Review, Progress, and Recommendations. International Journal of Artificial Intelligence in Education 26, 1 (2016), 160–169. https://doi.org/10.1007/s40593-015-0055-yGoogle ScholarCross Ref
- Randy Klaassen, Rieks op den Akker, Tine Lavrysen, and Susan van Wissen. 2013. User preferences for multi-device context-aware feedback in a digital coaching system. Journal on Multimodal User Interfaces 7, 3 (2013), 247–267. https://doi.org/10.1007/s12193-013-0125-0Google ScholarCross Ref
- Rafal Kocielnik, Lillian Xiao, Daniel Avrahami, and Gary Hsieh. 2018. Reflection Companion: A Conversational System for Engaging Users in Reflection on Physical Activity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 2 (2018), 1–26. https://doi.org/10.1145/3214273Google ScholarDigital Library
- Ascan F Koerner and Mary Anne Fitzpatrick. 2006. The SAGE Handbook of Conflict Communication: Integrating Theory, Research, and Practice. (2006), 159–184. https://doi.org/10.4135/9781412976176.n6Google ScholarCross Ref
- Ascan F Koerner, Paul Schrodt, and Mary Anne Fitzpatrick. 2017. Engaging Theories in Family Communication. (2017), 142–153. https://doi.org/10.4324/9781315204321-13Google ScholarCross Ref
- Joy Koesten, Paul Schrodt, and Debra J. Ford. 2009. Cognitive Flexibility as a Mediator of Family Communication Environments and Young Adults’ Well-Being. Health Communication 24, 1 (2009), 82–94. https://doi.org/10.1080/10410230802607024Google ScholarCross Ref
- Mareike Kritzler, Jack Hodges, Dan Yu, Kimberly Garcia, Hemant Shukla, and Florian Michahelles. 2019. Digital Companion for Industry. Companion Proceedings of The 2019 World Wide Web Conference (2019), 663–667. https://doi.org/10.1145/3308560.3316510Google ScholarDigital Library
- Kyong-Ah Kwon, Suejung Han, Hyun-Joo Jeon, and Gary E. Bingham. 2013. Mothers’ and fathers’ parenting challenges, strategies, and resources in toddlerhood. Early Child Development and Care 183, 3-4 (2013), 415–429. https://doi.org/10.1080/03004430.2012.711591Google ScholarCross Ref
- Taeahn Kwon, Minkyung Jeong, Eon-Suk Ko, and Youngki Lee. 2022. Captivate! Contextual Language Guidance for Parent-Child Interaction. arXiv (2022). https://doi.org/10.1145/3491102.3501865 arXiv:2202.06806Google ScholarDigital Library
- Briege M. Lagan, Marlene Sinclair, and W. George Kernohan. 2010. Internet Use in Pregnancy Informs Women’s Decision Making: A Web‐Based Survey. Birth 37, 2 (2010), 106–115. https://doi.org/10.1111/j.1523-536x.2010.00390.xGoogle ScholarCross Ref
- James Landay, Yuanchun Shi, Donald J Patterson, Yvonne Rogers, Xing Xie, Mashfiqui Rabbi, Shahid Ali, Tanzeem Choudhury, and Ethan Berke. 2011. Passive and In-Situ assessment of mental and physical well-being using mobile sensors. Proceedings of the 13th international conference on Ubiquitous computing 2011 (2011), 385–394. https://doi.org/10.1145/2030112.2030164Google ScholarDigital Library
- Susan H. Landry, Cynthia L. Miller-Loncar, Karen E. Smith, and Paul R. Swank. 2002. The Role of Early Parenting in Children’s Development of Executive Processes. Developmental Neuropsychology 21, 1 (2002), 15–41. https://doi.org/10.1207/s15326942dn2101_2Google ScholarCross Ref
- Susan H. Landry, Karen E. Smith, and Paul R. Swank. 2006. Responsive Parenting: Establishing Early Foundations for Social, Communication, and Independent Problem-Solving Skills. Developmental Psychology 42, 4 (2006), 627–642. https://doi.org/10.1037/0012-1649.42.4.627Google ScholarCross Ref
- Nicholas Lane, Mashfiqui Mohammod, Mu Lin, Xiaochao Yang, Hong Lu, Shahid Ali, Afsaneh Doryab, Ethan Berke, Tanzeem Choudhury, and Andrew Campbell. 2011. BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing. Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare (2011). https://doi.org/10.4108/icst.pervasivehealth.2011.246161Google ScholarCross Ref
- Janice Langan‐Fox, Jeromy Anglim, and John R. Wilson. 2004. Mental models, team mental models, and performance: Process, development, and future directions. Human Factors and Ergonomics in Manufacturing & Service Industries 14, 4 (2004), 331–352. https://doi.org/10.1002/hfm.20004Google ScholarCross Ref
- Christelle Langley, Bogdan Ionut Cirstea, Fabio Cuzzolin, and Barbara J. Sahakian. 2022. Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review. Frontiers in Artificial Intelligence 5 (2022), 778852. https://doi.org/10.3389/frai.2022.778852Google ScholarCross Ref
- Charlotte P Lee, Steve Poltrock, Louise Barkhuus, Marcos Borges, Wendy Kellogg, Kathleen O’Leary, Arpita Bhattacharya, Sean A Munson, Jacob O Wobbrock, and Wanda Pratt. 2017. Design Opportunities for Mental Health Peer Support Technologies. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (2017), 1470–1484. https://doi.org/10.1145/2998181.2998349Google ScholarDigital Library
- Kwan Min Lee, Younbo Jung, Jaywoo Kim, and Sang Ryong Kim. 2006. Are physically embodied social agents better than disembodied social agents?: The effects of physical embodiment, tactile interaction, and people’s loneliness in human–robot interaction. International Journal of Human-Computer Studies 64, 10 (2006), 962–973. https://doi.org/10.1016/j.ijhcs.2006.05.002Google ScholarDigital Library
- Yi-Chieh Lee, Naomi Yamashita, and Yun Huang. 2020. Designing a Chatbot as a Mediator for Promoting Deep Self-Disclosure to a Real Mental Health Professional. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1 (2020), 1–27. https://doi.org/10.1145/3392836Google ScholarDigital Library
- Yi-Chieh Lee, Naomi Yamashita, and Yun Huang. 2021. Exploring the Effects of Incorporating Human Experts to Deliver Journaling Guidance through a Chatbot. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–27. https://doi.org/10.1145/3449196Google ScholarDigital Library
- Yi-Chieh Lee, Naomi Yamashita, Yun Huang, and Wai Fu. 2020. "I Hear You, I Feel You": Encouraging Deep Self-disclosure through a Chatbot. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020), 1–12. https://doi.org/10.1145/3313831.3376175Google ScholarDigital Library
- Patty Leijten, Frances Gardner, G.J. Melendez-Torres, Jolien van Aar, Judy Hutchings, Susanne Schulz, Wendy Knerr, and Geertjan Overbeek. 2018. Meta-Analyses: Key Parenting Program Components for Disruptive Child Behavior. Journal of the American Academy of Child & Adolescent Psychiatry 58, 2 (2018), 180–190. https://doi.org/10.1016/j.jaac.2018.07.900Google ScholarCross Ref
- Patty Leijten, Frances Gardner, G.J. Melendez-Torres, Jolien van Aar, Judy Hutchings, Susanne Schulz, Wendy Knerr, and Geertjan Overbeek. 2019. Meta-Analyses: Key Parenting Program Components for Disruptive Child Behavior. Journal of the American Academy of Child & Adolescent Psychiatry 58, 2 (2019), 180–190. https://doi.org/10.1016/j.jaac.2018.07.900Google ScholarCross Ref
- Patty Leijten, G.J. Melendez‐Torres, Frances Gardner, Jolien Aar, Susanne Schulz, and Geertjan Overbeek. 2018. Are Relationship Enhancement and Behavior Management “The Golden Couple” for Disruptive Child Behavior? Two Meta‐analyses. Child Development 89, 6 (2018), 1970–1982. https://doi.org/10.1111/cdev.13051Google ScholarCross Ref
- Patty Leijten, G. J. Melendez‐Torres, and Frances Gardner. 2021. Research Review: The most effective parenting program content for disruptive child behavior – a network meta‐analysis. Journal of Child Psychology and Psychiatry 63, 2 (2021), 132–142. https://doi.org/10.1111/jcpp.13483Google ScholarCross Ref
- Iolanda Leite, Carlos Martinho, and Ana Paiva. 2013. Social Robots for Long-Term Interaction: A Survey. International Journal of Social Robotics 5, 2 (2013), 291–308. https://doi.org/10.1007/s12369-013-0178-yGoogle ScholarCross Ref
- Liliana J. Lengua, Elizabeth Honorado, and Nicole R. Bush. 2007. Contextual risk and parenting as predictors of effortful control and social competence in preschool children. Journal of Applied Developmental Psychology 28, 1 (2007), 40–55. https://doi.org/10.1016/j.appdev.2006.10.001Google ScholarCross Ref
- Matthew D. Lerner, Tiffany L. Hutchins, and Patricia A. Prelock. 2011. Brief Report: Preliminary Evaluation of the Theory of Mind Inventory and its Relationship to Measures of Social Skills. Journal of Autism and Developmental Disorders 41, 4 (2011), 512–517. https://doi.org/10.1007/s10803-010-1066-zGoogle ScholarCross Ref
- Ron C. Li, Steven M. Asch, and Nigam H. Shah. 2020. Developing a delivery science for artificial intelligence in healthcare. npj Digital Medicine 3, 1 (2020), 107. https://doi.org/10.1038/s41746-020-00318-yGoogle ScholarCross Ref
- Joseph Lindley and Paul Coulton. 2015. Back to the future: 10 years of design fiction. Proceedings of the 2015 British HCI Conference (2015), 210–211. https://doi.org/10.1145/2783446.2783592Google ScholarDigital Library
- Gale M. Lucas, Albert Rizzo, Jonathan Gratch, Stefan Scherer, Giota Stratou, Jill Boberg, and Louis-Philippe Morency. 2017. Reporting Mental Health Symptoms: Breaking Down Barriers to Care with Virtual Human Interviewers. Frontiers in Robotics and AI 4 (2017), 51. https://doi.org/10.3389/frobt.2017.00051Google ScholarCross Ref
- Andrés Lucero. 2015. Human-Computer Interaction – INTERACT 2015, 15th IFIP TC 13 International Conference, Bamberg, Germany, September 14-18, 2015, Proceedings, Part II. Lecture Notes in Computer Science (2015), 231–248. https://doi.org/10.1007/978-3-319-22668-2_19Google ScholarDigital Library
- Ewa Luger and Abigail Sellen. 2016. "Like Having a Really Bad PA": The Gulf between User Expectation and Experience of Conversational Agents. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (2016), 5286–5297. https://doi.org/10.1145/2858036.2858288Google ScholarDigital Library
- Deborah Lupton. 2021. The Routledge Companion to Digital Media and Children. (2021), 393–402. https://doi.org/10.4324/9781351004107-37Google ScholarCross Ref
- Suniya S. Luthar, Dante Cicchetti, and Bronwyn Becker. 2000. The Construct of Resilience: A Critical Evaluation and Guidelines for Future Work. Child Development 71, 3 (2000), 543–562. https://doi.org/10.1111/1467-8624.00164Google ScholarCross Ref
- Trivette C. M, Dunst C. J, Herin C. E. O., and Hamby D. W.2009. Characteristics and Consequences of Adult Learning Methods and Strategies. (2009).Google Scholar
- Kathleen Watson MacDonell and Ronald J. Prinz. 2017. A Review of Technology-Based Youth and Family-Focused Interventions. Clinical Child and Family Psychology Review 20, 2 (2017), 185–200. https://doi.org/10.1007/s10567-016-0218-xGoogle ScholarCross Ref
- Naja A Mack, Dekita G Moon Rembert, Robert Cummings, and Juan E Gilbert. 2019. Co-Designing an Intelligent Conversational History Tutor with Children. Proceedings of the 18th ACM International Conference on Interaction Design and Children (2019), 482–487. https://doi.org/10.1145/3311927.3325336Google ScholarDigital Library
- Wendy E Mackay, Stephen Brewster, Susanne Bødker, Jakob E Bardram, Mads Frost, Károly Szántó, Maria Faurholt-Jepsen, Maj Vinberg, and Lars Vedel Kessing. 2013. Designing mobile health technology for bipolar disorder. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2013), 2627–2636. https://doi.org/10.1145/2470654.2481364Google ScholarDigital Library
- Nitin Madnani, Beata Beigman Klebanov, Anastassia Loukina, Binod Gyawali, Patrick Lange, John Sabatini, and Michael Flor. 2019. My Turn To Read: An Interleaved E-book Reading Tool for Developing and Struggling Readers. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations (2019), 141–146. https://doi.org/10.18653/v1/p19-3024Google ScholarCross Ref
- Gloria Mark, Susan Fussell, Cliff Lampe, Juan Pablo Hourcade, Caroline Appert, Daniel Wigdor, Amanda Purington, Jessie G Taft, Shruti Sannon, Natalya N Bazarova, and Samuel Hardman Taylor. 2017. "Alexa is my new BFF": Social Roles, User Satisfaction, and Personification of the Amazon Echo. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (2017), 2853–2859. https://doi.org/10.1145/3027063.3053246Google ScholarDigital Library
- Jack Martin. 2004. Self-Regulated Learning, Social Cognitive Theory, and Agency. Educational Psychologist 39, 2 (2004), 135–145. https://doi.org/10.1207/s15326985ep3902_4Google ScholarCross Ref
- Denita R. Maughan, Elizabeth Christiansen, William R. Jenson, Daniel Olympia, and Elaine Clark. 2005. Behavioral Parent Training as a Treatment for Externalizing Behaviors and Disruptive Behavior Disorders: A Meta-Analysis. School Psychology Review 34, 3 (2005), 267–286. https://doi.org/10.1080/02796015.2005.12086287Google ScholarCross Ref
- Anne M. Mauricio, Nancy A. Gonzales, and Irwin N. Sandler. 2018. Preventive Parenting Interventions: Advancing Conceptualizations of Participation and Enhancing Reach. Prevention Science 19, 5 (2018), 603–608. https://doi.org/10.1007/s11121-018-0876-7Google ScholarCross Ref
- Kathleen McCoy, E. Mark Cummings, and Patrick T. Davies. 2009. Constructive and destructive marital conflict, emotional security and children’s prosocial behavior. Journal of Child Psychology and Psychiatry 50, 3 (2009), 270–279. https://doi.org/10.1111/j.1469-7610.2008.01945.xGoogle ScholarCross Ref
- Kathleen P McCoy, Melissa R W George, E Mark Cummings, and Patrick T Davies. 2013. Constructive and Destructive Marital Conflict, Parenting, and Children’s School and Social Adjustment: Marital Conflict and Parenting. Social Development 22, 4 (2013), n/a–n/a. https://doi.org/10.1111/sode.12015Google ScholarCross Ref
- Lucy McGoron and Steven J. Ondersma. 2015. Reviewing the need for technological and other expansions of evidence-based parent training for young children. Children and Youth Services Review 59 (2015), 71–83. https://doi.org/10.1016/j.childyouth.2015.10.012Google ScholarCross Ref
- James P. McHale and Karina Irace. 2011. Coparenting: A conceptual and clinical examination of family systems. (2011), 15–37. https://doi.org/10.1037/12328-001Google ScholarCross Ref
- Ali Meghdari, Azadeh Shariati, Minoo Alemi, Ali Amoozandeh Nobaveh, Mobin Khamooshi, and Behrad Mozaffari. 2018. Design Performance Characteristics of a Social Robot Companion “Arash” for Pediatric Hospitals. International Journal of Humanoid Robotics 15, 05 (2018), 1850019. https://doi.org/10.1142/s0219843618500196Google ScholarCross Ref
- James A. Mercy and Janet Saul. 2009. Creating a Healthier Future Through Early Interventions for Children. JAMA 301, 21 (2009), 2262–2264. https://doi.org/10.1001/jama.2009.803Google ScholarCross Ref
- Joseph E. Michaelis and Bilge Mutlu. 2018. Reading socially: Transforming the in-home reading experience with a learning-companion robot. Science Robotics 3, 21 (2018). https://doi.org/10.1126/scirobotics.aat5999Google ScholarCross Ref
- Daniel Michelson, Clare Davenport, Janine Dretzke, Jane Barlow, and Crispin Day. 2013. Do Evidence-Based Interventions Work When Tested in the “Real World?” A Systematic Review and Meta-analysis of Parent Management Training for the Treatment of Child Disruptive Behavior. Clinical Child and Family Psychology Review 16, 1 (2013), 18–34. https://doi.org/10.1007/s10567-013-0128-0Google ScholarCross Ref
- Sue Miller and Kay Sambell. 2003. What do parents feel they need? Implications of parents’ perspectives for the facilitation of parenting programmes. Children & Society 17, 1 (2003), 32–44. https://doi.org/10.1002/chi.726Google ScholarCross Ref
- Jyoti Mishra, David Allen, and Alan Pearman. 2014. Information seeking, use, and decision making: Information Seeking, Use, and Decision Making. Journal of the Association for Information Science and Technology 66, 4 (2014), 662–673. https://doi.org/10.1002/asi.23204Google ScholarDigital Library
- Carole Mockford and Jane Barlow. 2004. Parenting programmes: some unintended consequences. Primary Health Care Research & Development 5, 3 (2004), 219–227. https://doi.org/10.1191/1463423604pc200oaGoogle ScholarCross Ref
- Shiwali Mohan, Anusha Venkatakrishnan, and Andrea L. Hartzler. 2020. Designing an AI Health Coach and Studying Its Utility in Promoting Regular Aerobic Exercise. ACM Transactions on Interactive Intelligent Systems (TiiS) 10, 2 (2020), 1–30. https://doi.org/10.1145/3366501 arXiv:1910.04836Google ScholarDigital Library
- Joao Luis Zeni Montenegro, Cristiano André da Costa, and Rodrigo da Rosa Righi. 2019. Survey of conversational agents in health. Expert Systems with Applications 129 (2019), 56–67. https://doi.org/10.1016/j.eswa.2019.03.054Google ScholarDigital Library
- A. Morawska, L. Winter, and M. R. Sanders. 2009. Parenting knowledge and its role in the prediction of dysfunctional parenting and disruptive child behaviour. Child: Care, Health and Development 35, 2 (2009), 217–226. https://doi.org/10.1111/j.1365-2214.2008.00929.xGoogle ScholarCross Ref
- Amanda Sheffield Morris, Jens E. Jespersen, Kelly T. Cosgrove, Erin L. Ratliff, and Kara L. Kerr. 2020. Parent Education: What We Know and Moving Forward for Greatest Impact. Family Relations 69, 3 (2020), 520–542. https://doi.org/10.1111/fare.12442Google ScholarCross Ref
- Benedicte Mouton, Laurie Loop, Marie Stievenart, and Isabelle Roskam. 2018. Parenting Programs to Reduce Young Children’s Externalizing Behavior: A Meta-Analytic Review of Their Behavioral or Cognitive Orientation. Child & Family Behavior Therapy 40, 2 (2018), 115–147. https://doi.org/10.1080/07317107.2018.1477348Google ScholarCross Ref
- Sarah Ann Mummah, Thomas N Robinson, Abby C King, Christopher D Gardner, and Stephen Sutton. 2016. IDEAS (Integrate, Design, Assess, and Share): A Framework and Toolkit of Strategies for the Development of More Effective Digital Interventions to Change Health Behavior. Journal of Medical Internet Research 18, 12 (2016), e317. https://doi.org/10.2196/jmir.5927Google ScholarCross Ref
- Naomi Narramore. 2008. Meeting the emotional needs of parents who have a child with complex needs. Journal of Children’s and Young People’s Nursing 2, 3 (2008), 103–107. https://doi.org/10.12968/jcyn.2008.2.3.28701Google ScholarCross Ref
- Cameron L. Neece, Shulamite A. Green, and Bruce L. Baker. 2012. Parenting Stress and Child Behavior Problems: A Transactional Relationship Across Time. American Journal on Intellectual and Developmental Disabilities 117, 1 (2012), 48–66. https://doi.org/10.1352/1944-7558-117.1.48Google ScholarCross Ref
- S. Katherine Nelson, Kostadin Kushlev, and Sonja Lyubomirsky. 2014. The Pains and Pleasures of Parenting: When, Why, and How Is Parenthood Associated With More or Less Well-Being?Psychological Bulletin 140, 3 (2014), 846–895. https://doi.org/10.1037/a0035444Google ScholarCross Ref
- Hien Nguyen and Judith Masthoff. 2009. Designing empathic computers: the effect of multimodal empathic feedback using animated agent. Proceedings of the 4th International Conference on Persuasive Technology (2009), 7. https://doi.org/10.1145/1541948.1541958Google ScholarDigital Library
- Thin Nguyen, Bridianne O’Dea, Mark Larsen, Dinh Phung, Svetha Venkatesh, and Helen Christensen. 2017. Using linguistic and topic analysis to classify sub-groups of online depression communities. Multimedia Tools and Applications 76, 8 (2017), 10653–10676. https://doi.org/10.1007/s11042-015-3128-xGoogle ScholarDigital Library
- Jan M Nicholson, Donna Berthelsen, Kate E Williams, and Vicky Abad. 2010. National study of an early parenting intervention: implementation differences on parent and child outcomes: parenting program implementation.Prevention science : the official journal of the Society for Prevention Research 11, 4 (2010), 360–70. https://doi.org/10.1007/s11121-010-0181-6Google ScholarCross Ref
- Christa C. Nieuwboer, Ruben G. Fukkink, and Jo M.A. Hermanns. 2013. Online programs as tools to improve parenting: A meta-analytic review. Children and Youth Services Review 35, 11 (2013), 1823–1829. https://doi.org/10.1016/j.childyouth.2013.08.008Google ScholarCross Ref
- Alicia L Nobles, Jeffrey J Glenn, Kamran Kowsari, Bethany A Teachman, and Laura E Barnes. 2018. Identification of Imminent Suicide Risk Among Young Adults using Text Messages. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems 2018 (2018), 1–11. https://doi.org/10.1145/3173574.3173987Google ScholarDigital Library
- Renee Noortman, Britta F Schulte, Paul Marshall, Saskia Bakker, and Anna L Cox. 2019. HawkEye - Deploying a Design Fiction Probe. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2019), 1–14. https://doi.org/10.1145/3290605.3300652Google ScholarDigital Library
- Thomas G. O’Connor. 2002. Annotation: The ‘effects’ of parenting reconsidered: findings, challenges, and applications. Journal of Child Psychology and Psychiatry 43, 5 (2002), 555–572. https://doi.org/10.1111/1469-7610.00046Google ScholarCross Ref
- Elizabeth B. Owens and Daniel S. Shaw. 2003. Predicting Growth Curves of Externalizing Behavior Across the Preschool Years. Journal of Abnormal Child Psychology 31, 6 (2003), 575–590. https://doi.org/10.1023/a:1026254005632Google ScholarCross Ref
- Christine O’Farrelly, Hilary Watt, Daphne Babalis, Marian J. Bakermans-Kranenburg, Beth Barker, Sarah Byford, Poushali Ganguli, Ellen Grimas, Jane Iles, Holly Mattock, Julia McGinley, Charlotte Phillips, Rachael Ryan, Stephen Scott, Jessica Smith, Alan Stein, Eloise Stevens, Marinus H. van IJzendoorn, Jane Warwick, and Paul G. Ramchandani. 2021. A Brief Home-Based Parenting Intervention to Reduce Behavior Problems in Young Children. JAMA Pediatrics 175, 6 (2021), 567–576. https://doi.org/10.1001/jamapediatrics.2020.6834Google ScholarCross Ref
- Rui Pan, Azadeh Forghani, Carman Neustaedter, Nick Strauss, and Ashley Guindon. 2015. The Family Board: An Information Sharing System for Family Members. Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing (2015), 207–210. https://doi.org/10.1145/2685553.2699008Google ScholarDigital Library
- Jeffrey G. Parker and Steven R. Asher. 1987. Peer Relations and Later Personal Adjustment: Are Low-Accepted Children At Risk?Psychological Bulletin 102, 3 (1987), 357–389. https://doi.org/10.1037/0033-2909.102.3.357Google ScholarCross Ref
- Peggy L Parks and Vincent L Smeriglio. 1986. Relationships among Parenting Knowledge, Quality of Stimulation in the Home and Infant Development. Family Relations 35, 3 (1986), 411. https://doi.org/10.2307/584369Google ScholarCross Ref
- Gerald R. Patterson. 1990. Depression and Aggression in Family interaction. (1990). https://doi.org/10.4324/9780203771334Google ScholarCross Ref
- Sabine Payr. 2013. Your Virtual Butler, The Making-of. Lecture Notes in Computer Science (2013), 134–178. https://doi.org/10.1007/978-3-642-37346-6_11Google ScholarCross Ref
- James Pierce and Carl DiSalvo. 2018. Addressing Network Anxieties with Alternative Design Metaphors. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (2018), 1–13. https://doi.org/10.1145/3173574.3174123Google ScholarDigital Library
- Laura Pina, Kael Rowan, Asta Roseway, Paul Johns, Gillian R. Hayes, and Mary Czerwinski. 2014. Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth ’14. Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare (2014), 17–24. https://doi.org/10.4108/icst.pervasivehealth.2014.254958Google ScholarDigital Library
- Laura Pina, Kael Rowan, Asta Roseway, Paul Johns, Gillian R. Hayes, and Mary Czerwinski. 2014. Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth ’14. Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare (2014), 17–24. https://doi.org/10.4108/icst.pervasivehealth.2014.254958Google ScholarDigital Library
- Laura R Pina, Sang-Wha Sien, Teresa Ward, Jason C Yip, Sean A Munson, James Fogarty, and Julie A Kientz. 2017. From Personal Informatics to Family Informatics. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (2017), 2300–2315. https://doi.org/10.1145/2998181.2998362Google ScholarDigital Library
- Martin Pinquart and Daniela Teubert. 2010. Effects of Parenting Education With Expectant and New Parents: A Meta-Analysis. Journal of Family Psychology 24, 3 (2010), 316–327. https://doi.org/10.1037/a0019691Google ScholarCross Ref
- Patrycja J. Piotrowska, L. A. Tully, R. Lenroot, E. Kimonis, D. Hawes, C. Moul, P. J. Frick, V. Anderson, and M. R. Dadds. 2017. Mothers, Fathers, and Parental Systems: A Conceptual Model of Parental Engagement in Programmes for Child Mental Health—Connect, Attend, Participate, Enact (CAPE). Clinical Child and Family Psychology Review 20, 2 (2017), 146–161. https://doi.org/10.1007/s10567-016-0219-9Google ScholarCross Ref
- Martin Porcheron. 2021. What’s in a name and does CUI matter?CUI 2021 - 3rd Conference on Conversational User Interfaces (2021), 1–3. https://doi.org/10.1145/3469595.3469619Google ScholarDigital Library
- Enola K. Proctor, John Landsverk, Gregory Aarons, David Chambers, Charles Glisson, and Brian Mittman. 2009. Implementation Research in Mental Health Services: an Emerging Science with Conceptual, Methodological, and Training challenges. Administration and Policy in Mental Health and Mental Health Services Research 36, 1 (2009), 24–34. https://doi.org/10.1007/s10488-008-0197-4Google ScholarCross Ref
- Stefan Ramaekers and Naomi Hodgson. 2020. Parenting apps and the depoliticisation of the parent. Families, Relationships and Societies 9, 1 (2020), 107–124. https://doi.org/10.1332/204674319x15681326073976Google ScholarCross Ref
- Pavani Rangachari, Peter Rissing, and Karl Rethemeyer. 2013. Awareness of Evidence-Based Practices Alone Does Not Translate to Implementation: Insights From Implementation Research. Quality Management in Health Care 22, 2 (2013), 117–125. https://doi.org/10.1097/qmh.0b013e31828bc21dGoogle ScholarCross Ref
- Amon Rapp. 2018. Design fictions for behaviour change: exploring the long-term impacts of technology through the creation of fictional future prototypes. Behaviour & Information Technology 38, 3 (2018), 244–272. https://doi.org/10.1080/0144929x.2018.1526970Google ScholarCross Ref
- Amon Rapp and Maurizio Tirassa. 2017. Know Thyself: A Theory of the Self for Personal Informatics. Human–Computer Interaction 32, 5-6 (2017), 335–380. https://doi.org/10.1080/07370024.2017.1285704Google ScholarDigital Library
- Minjin Rheu, Ji Youn Shin, Wei Peng, and Jina Huh-Yoo. 2021. Systematic Review: Trust-Building Factors and Implications for Conversational Agent Design. International Journal of Human–Computer Interaction 37, 1 (2021), 81–96. https://doi.org/10.1080/10447318.2020.1807710Google ScholarCross Ref
- Nikki Rickard, Hussain-Abdulah Arjmand, David Bakker, and Elizabeth Seabrook. 2016. Development of a Mobile Phone App to Support Self-Monitoring of Emotional Well-Being: A Mental Health Digital Innovation. JMIR Mental Health 3, 4 (2016), e49. https://doi.org/10.2196/mental.6202Google ScholarCross Ref
- Ronda Ringfort-Felner, Matthias Laschke, Shadan Sadeghian, and Marc Hassenzahl. 2022. Kiro: A Design Fiction to Explore Social Conversation with Voice Assistants. Proceedings of the ACM on Human-Computer Interaction 6, GROUP (2022), 1–21. https://doi.org/10.1145/3492852Google ScholarDigital Library
- Lee M. Ritterband, Frances P. Thorndike, Daniel J. Cox, Boris P. Kovatchev, and Linda A. Gonder-Frederick. 2009. A Behavior Change Model for Internet Interventions. Annals of Behavioral Medicine 38, 1 (2009), 18. https://doi.org/10.1007/s12160-009-9133-4Google ScholarCross Ref
- Hayley Robinson, Bruce MacDonald, and Elizabeth Broadbent. 2014. The Role of Healthcare Robots for Older People at Home: A Review. International Journal of Social Robotics 6, 4 (2014), 575–591. https://doi.org/10.1007/s12369-014-0242-2Google ScholarCross Ref
- Diego Fernando Rojas-Gualdron. 2022. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. National Academy of Medicine. Una reseña. CES Medicina 36, 1 (2022), 76–78. https://doi.org/10.21615/cesmedicina.6571Google ScholarCross Ref
- Rod D. Roscoe and Michelene T. H. Chi. 2007. Understanding Tutor Learning: Knowledge-Building and Knowledge-Telling in Peer Tutors’ Explanations and Questions. Review of Educational Research 77, 4 (2007), 534–574. https://doi.org/10.3102/0034654307309920Google ScholarCross Ref
- Elisa Rubegni, Laura Malinverni, and Jason Yip. 2022. “Don’t let the robots walk our dogs, but it’s ok for them to do our homework”: children’s perceptions, fears, and hopes in social robots.Interaction Design and Children (2022), 352–361. https://doi.org/10.1145/3501712.3529726Google ScholarDigital Library
- Jessie Rudi, Yaliu He, Jodi Dworkin, and Jennifer Doty. 2018. How Useful Is It? Differences in Parents’ Perceptions of Parenting Information Sources. Journal of Human Sciences and Extension (2018). https://doi.org/10.54718/besq7971Google ScholarCross Ref
- Dathan D Rush, MʼLisa L Shelden, and Barbara E Hanft. 2003. Coaching Families and Colleagues: A Process for Collaboration in Natural Settings. Infants & Young Children 16, 1 (2003), 33–47. https://doi.org/10.1097/00001163-200301000-00005Google ScholarCross Ref
- Michael Rutter. 2007. Resilience, competence, and coping. Child Abuse & Neglect 31, 3 (2007), 205–209. https://doi.org/10.1016/j.chiabu.2007.02.001Google ScholarCross Ref
- Farida Sabry, Tamer Eltaras, Wadha Labda, Khawla Alzoubi, and Qutaibah Malluhi. 2022. Machine Learning for Healthcare Wearable Devices: The Big Picture. Journal of Healthcare Engineering 2022 (2022), 4653923. https://doi.org/10.1155/2022/4653923Google ScholarCross Ref
- Herman Saksono, Carmen Castaneda-Sceppa, Jessica Hoffman, Vivien Morris, Magy Seif El-Nasr, and Andrea G Parker. 2020. Storywell: Designing for Family Fitness App Motivation by Using Social Rewards and Reflection. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020), 1–13. https://doi.org/10.1145/3313831.3376686Google ScholarDigital Library
- Pedro Sanches, Axel Janson, Pavel Karpashevich, Camille Nadal, Chengcheng Qu, Claudia Daudén Roquet, Muhammad Umair, Charles Windlin, Gavin Doherty, Kristina Höök, and Corina Sas. 2019. HCI and Affective Health. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2019), 1–17. https://doi.org/10.1145/3290605.3300475Google ScholarDigital Library
- Matthew R. Sanders. 1999. Triple P-Positive Parenting Program: Towards an Empirically Validated Multilevel Parenting and Family Support Strategy for the Prevention of Behavior and Emotional Problems in Children. Clinical Child and Family Psychology Review 2, 2 (1999), 71–90. https://doi.org/10.1023/a:1021843613840Google ScholarCross Ref
- Matthew R Sanders. 2003. Triple P – Positive Parenting Program: A population approach to promoting competent parenting. Australian e-Journal for the Advancement of Mental Health 2, 3 (2003), 127–143. https://doi.org/10.5172/jamh.2.3.127Google ScholarCross Ref
- Matthew R. Sanders. 2008. Triple P-Positive Parenting Program as a Public Health Approach to Strengthening Parenting. Journal of Family Psychology 22, 4 (2008), 506–517. https://doi.org/10.1037/0893-3200.22.3.506Google ScholarCross Ref
- Matthew R. Sanders, Sabine Baker, and Karen M.T. Turner. 2012. A randomized controlled trial evaluating the efficacy of Triple P Online with parents of children with early-onset conduct problems. Behaviour Research and Therapy 50, 11 (2012), 675–684. https://doi.org/10.1016/j.brat.2012.07.004Google ScholarCross Ref
- Matthew R. Sanders and Ted Glynn. 1981. TRAINING PARENTS IN BEHAVIORAL SELF‐MANAGEMENT: AN ANALYSIS OF GENERALIZATION AND MAINTENANCE. Journal of Applied Behavior Analysis 14, 3 (1981), 223–237. https://doi.org/10.1901/jaba.1981.14-223Google ScholarCross Ref
- Matthew R. Sanders and James N. Kirby. 2012. Consumer Engagement and the Development, Evaluation, and Dissemination of Evidence-Based Parenting Programs. Behavior Therapy 43, 2 (2012), 236–250. https://doi.org/10.1016/j.beth.2011.01.005Google ScholarCross Ref
- Matthew R. Sanders, Carol Markie-Dadds, Lucy A. Tully, and William Bor. 2000. The Triple P-Positive Parenting Program: A Comparison of Enhanced, Standard, and Self-Directed Behavioral Family Intervention for Parents of Children With Early Onset Conduct Problems. Journal of Consulting and Clinical Psychology 68, 4 (2000), 624–640. https://doi.org/10.1037/0022-006x.68.4.624Google ScholarCross Ref
- Matthew R. Sanders and Karen M. T. Turner. 2018. Handbook of Parenting and Child Development Across the Lifespan. (2018), 3–26. https://doi.org/10.1007/978-3-319-94598-9_1Google ScholarCross Ref
- Irwin Sandler, Alexandra Ingram, Sharlene Wolchik, Jenn‐Yun Tein, and Emily Winslow. 2015. Long‐Term Effects of Parenting‐Focused Preventive Interventions to Promote Resilience of Children and Adolescents. Child Development Perspectives 9, 3 (2015), 164–171. https://doi.org/10.1111/cdep.12126Google ScholarCross Ref
- Irwin N. Sandler, Erin N. Schoenfelder, Sharlene A. Wolchik, and David P. MacKinnon. 2011. Long-Term Impact of Prevention Programs to Promote Effective Parenting: Lasting Effects but Uncertain Processes. Annual Review of Psychology 62, 1 (2011), 299–329. https://doi.org/10.1146/annurev.psych.121208.131619Google ScholarCross Ref
- Brian Scassellati, Laura Boccanfuso, Chien-Ming Huang, Marilena Mademtzi, Meiying Qin, Nicole Salomons, Pamela Ventola, and Frederick Shic. 2018. Improving social skills in children with ASD using a long-term, in-home social robot. Science Robotics 3, 21 (2018). https://doi.org/10.1126/scirobotics.aat7544Google ScholarCross Ref
- Alice C. Schermerhorn and E. Mark Cummings. 2008. Transactional Family Dynamics: A New Framework for Conceptualizing Family Influence Processes. Advances in Child Development and Behavior 36 (2008), 187–250. https://doi.org/10.1016/s0065-2407(08)00005-0Google ScholarCross Ref
- Mark R Scholten, Saskia M Kelders, and Julia EWC Van Gemert-Pijnen. 2017. Self-Guided Web-Based Interventions: Scoping Review on User Needs and the Potential of Embodied Conversational Agents to Address Them. Journal of Medical Internet Research 19, 11 (2017), e383. https://doi.org/10.2196/jmir.7351Google ScholarCross Ref
- Paul Schrodt, Andrew M. Ledbetter, Kodiane A. Jernberg, Lara Larson, Nicole Brown, and Katie Glonek. 2009. Family communication patterns as mediators of communication competence in the parent—child relationship. Journal of Social and Personal Relationships 26, 6-7 (2009), 853–874. https://doi.org/10.1177/0265407509345649Google ScholarCross Ref
- Jessica Schroeder, Chelsey Wilkes, Kael Rowan, Arturo Toledo, Ann Paradiso, Mary Czerwinski, Gloria Mark, and Marsha M Linehan. 2018. Pocket Skills: A Conversational Mobile Web App To Support Dialectical Behavioral Therapy. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (2018), 1–15. https://doi.org/10.1145/3173574.3173972Google ScholarDigital Library
- Valarie M. Schroeder and Michelle L. Kelley. 2010. Family environment and parent‐child relationships as related to executive functioning in children. Early Child Development and Care 180, 10 (2010), 1285–1298. https://doi.org/10.1080/03004430902981512Google ScholarCross Ref
- Douglas Schuler, Myriam Lewkowicz, Markus Rohde, Ingrid Mulder, Karl Baumann, Benjamin Stokes, François Bar, and Ben Caldwell. 2017. Infrastructures of the Imagination: Community Design for Speculative Urban Technologies. Proceedings of the 8th International Conference on Communities and Technologies (2017), 266–269. https://doi.org/10.1145/3083671.3083700Google ScholarDigital Library
- Donald A. Schön. 1991. The Reflective Practitioner. (1991). https://doi.org/10.4324/9781315237473Google ScholarCross Ref
- Mark D. Seery, E. Alison Holman, and Roxane Cohen Silver. 2010. Whatever Does Not Kill Us: Cumulative Lifetime Adversity, Vulnerability, and Resilience. Journal of Personality and Social Psychology 99, 6 (2010), 1025–1041. https://doi.org/10.1037/a0021344Google ScholarCross Ref
- Wendy J. Serketich and Jean E. Dumas. 1996. The effectiveness of behavioral parent training to modify antisocial behavior in children: A meta-analysis. Behavior Therapy 27, 2 (1996), 171–186. https://doi.org/10.1016/s0005-7894(96)80013-xGoogle ScholarCross Ref
- Jenelle R. Shanley and Larissa N. Niec. 2010. Coaching Parents to Change: The Impact of In Vivo Feedback on Parents’ Acquisition of Skills. Journal of Clinical Child & Adolescent Psychology 39, 2 (2010), 282–287. https://doi.org/10.1080/15374410903532627Google ScholarCross Ref
- Sumita Sharma, Netta Iivari, Leena Ventä-Olkkonen, Heidi Hartikainen, and Marianne Kinnula. 2023. Inclusive Child-centered AI: Employing design futuring for Inclusive design of inclusive AI by and with children in Finland and India. arXiv (2023). https://doi.org/10.48550/arxiv.2304.08041 arXiv:2304.08041Google ScholarCross Ref
- Daniel S. Shaw, Kate Keenan, and Joan I. Vondra. 1994. Developmental Precursors of Externalizing Behavior: Ages 1 to 3. Developmental Psychology 30, 3 (1994), 355–364. https://doi.org/10.1037/0012-1649.30.3.355Google ScholarCross Ref
- Elizabeth C. Shelleby and Daniel S. Shaw. 2014. Outcomes of Parenting Interventions for Child Conduct Problems: A Review of Differential Effectiveness. Child Psychiatry & Human Development 45, 5 (2014), 628–645. https://doi.org/10.1007/s10578-013-0431-5Google ScholarCross Ref
- Masahiro Shiomi and Norihiro Hagita. 2017. Social acceptance toward a childcare support robot system: web-based cultural differences investigation and a field study in Japan. Advanced Robotics 31, 14 (2017), 727–738. https://doi.org/10.1080/01691864.2017.1345322Google ScholarCross Ref
- Ben Shneiderman. 2020. Bridging the Gap Between Ethics and Practice. ACM Transactions on Interactive Intelligent Systems (TiiS) 10, 4 (2020), 1–31. https://doi.org/10.1145/3419764Google ScholarDigital Library
- Ben Shneiderman. 2022. Human-Centered AI. (2022), 17–24. https://doi.org/10.1093/oso/9780192845290.003.0002Google ScholarCross Ref
- Elaine Short, Katelyn Swift-Spong, Jillian Greczek, Aditi Ramachandran, Alexandru Litoiu, Elena Corina Grigore, David Feil-Seifer, Samuel Shuster, Jin Joo Lee, Shaobo Huang, Svetlana Levonisova, Sarah Litz, Jamy Li, Gisele Ragusa, Donna Spruijt-Metz, Maja Matarić, and Brian Scassellati. 2014. How to Train Your DragonBot: Socially Assistive Robots for Teaching Children About Nutrition Through Play. The 23rd IEEE International Symposium on Robot and Human Interactive Communication (2014), 924–929. https://doi.org/10.1109/roman.2014.6926371Google ScholarCross Ref
- Heung-yeung Shum, Xiao-dong He, and Di Li. 2018. From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering 19, 1 (2018), 10–26. https://doi.org/10.1631/fitee.1700826Google ScholarCross Ref
- Eliane Siegenthaler, Thomas Munder, and Matthias Egger. 2012. Effect of Preventive Interventions in Mentally Ill Parents on the Mental Health of the Offspring: Systematic Review and Meta-Analysis. Journal of the American Academy of Child & Adolescent Psychiatry 51, 1 (2012), 8–17.e8. https://doi.org/10.1016/j.jaac.2011.10.018Google ScholarCross Ref
- Wan Hua Sim, Anthony F. Jorm, and Marie B. H. Yap. 2022. The Role of Parent Engagement in a Web-Based Preventive Parenting Intervention for Child Mental Health in Predicting Parenting, Parent and Child Outcomes. International Journal of Environmental Research and Public Health 19, 4 (2022), 2191. https://doi.org/10.3390/ijerph19042191Google ScholarCross Ref
- Ellen Skinner, Sandy Johnson, and Tatiana Snyder. 2005. Six Dimensions of Parenting: A Motivational Model. Parenting 5, 2 (2005), 175–235. https://doi.org/10.1207/s15327922par0502_3Google ScholarCross Ref
- Arietta Slade. 2005. Parental reflective functioning: An introduction. Attachment & Human Development 7, 3 (2005), 269–281. https://doi.org/10.1080/14616730500245906Google ScholarCross Ref
- Petr Slovák, Nikki Theofanopoulou, Alessia Cecchet, Peter Cottrell, Ferran Altarriba Bertran, Ella Dagan, Julian Childs, and Katherine Isbister. 2018. "I just let him cry...Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1–34. https://doi.org/10.1145/3274429Google ScholarDigital Library
- Rebecca Smith. 2017. The emergence of the quantified child. Discourse: Studies in the Cultural Politics of Education 38, 5 (2017), 701–712. https://doi.org/10.1080/01596306.2015.1136269Google ScholarCross Ref
- Wally Smith, Greg Wadley, Sarah Webber, Benjamin Tag, Vassilis Kostakos, Peter Koval, and James J. Gross. 2022. Digital Emotion Regulation in Everyday Life. CHI Conference on Human Factors in Computing Systems (2022), 1–15. https://doi.org/10.1145/3491102.3517573Google ScholarDigital Library
- Seokwoo Song, Seungho Kim, John Kim, Wonjeong Park, and Dongsun Yim. 2016. TalkLIME. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (2016), 304–315. https://doi.org/10.1145/2971648.2971650Google ScholarDigital Library
- Newton Spolaôr and Fabiane B.Vavassori Benitti. 2017. Robotics applications grounded in learning theories on tertiary education: A systematic review. Computers & Education 112 (2017), 97–107. https://doi.org/10.1016/j.compedu.2017.05.001Google ScholarCross Ref
- Howard Steele, Jordan Bate, Miriam Steele, Shanta Rishi Dube, Kerri Danskin, Hannah Knafo, Adella Nikitiades, Karen Bonuck, Paul Meissner, and Anne Murphy. 2016. Adverse Childhood Experiences, Poverty, and Parenting Stress. Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement 48, 1 (2016), 32–38. https://doi.org/10.1037/cbs0000034Google ScholarCross Ref
- Joseph H Stevens. 1984. Child Development Knowledge and Parenting Skills. Family Relations 33, 2 (1984), 237. https://doi.org/10.2307/583789Google ScholarCross Ref
- Shannon Wiltsey Stirman, Cassidy A. Gutner, Kirsten Langdon, and Jessica R. Graham. 2016. Bridging the Gap Between Research and Practice in Mental Health Service Settings: An Overview of Developments in Implementation Theory and Research. Behavior Therapy 47, 6 (2016), 920–936. https://doi.org/10.1016/j.beth.2015.12.001Google ScholarCross Ref
- T F Stokes and D M Baer. 1977. An implicit technology of generalization.Journal of applied behavior analysis 10, 2 (1977), 349–67. https://doi.org/10.1901/jaba.1977.10-349Google ScholarCross Ref
- Kevin M Storer, Tejinder K Judge, and Stacy M Branham. 2020. "All in the Same Boat": Tradeoffs of Voice Assistant Ownership for Mixed-Visual-Ability Families. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020), 1–14. https://doi.org/10.1145/3313831.3376225Google ScholarDigital Library
- Melissa L. Sturge‐Apple, Patrick T. Davies, and E. Mark Cummings. 2006. Impact of Hostility and Withdrawal in Interparental Conflict on Parental Emotional Unavailability and Children’s Adjustment Difficulties. Child Development 77, 6 (2006), 1623–1641. https://doi.org/10.1111/j.1467-8624.2006.00963.xGoogle ScholarCross Ref
- Saravid A/L Suchaad, Koichiro Mashiko, Nordinah Binti Ismail, and Mohamad Hafizat Zainal Abidin. 2018. Blockchain Use in Home Automation for Children Incentives in Parental Control. Proceedings of the 2018 International Conference on Machine Learning and Machine Intelligence (2018), 50–53. https://doi.org/10.1145/3278312.3278326Google ScholarDigital Library
- Arminda Suárez, Sonia Byrne, and María José Rodrigo. 2018. Effectiveness of a Universal Web-based Parenting Program to Promote Positive Parenting: Patterns and Predictors on Program Satisfaction. Journal of Child and Family Studies 27, 10 (2018), 3345–3357. https://doi.org/10.1007/s10826-018-1162-9Google ScholarCross Ref
- Kate Sweeny, Patrick J Carroll, and James A Shepperd. 2006. Is Optimism Always Best?: Future Outlooks and Preparedness. Current Directions in Psychological Science 15, 6 (2006), 302–306. https://doi.org/10.1111/j.1467-8721.2006.00457.xGoogle ScholarCross Ref
- Mariana Aki Tamashiro, Maarten Van Mechelen, Marie-Monique Schaper, and Ole Sejer Iversen. 2021. Introducing Teenagers to Machine Learning through Design Fiction: An Exploratory Case Study. Interaction Design and Children (2021), 471–475. https://doi.org/10.1145/3459990.3465193Google ScholarDigital Library
- Ted K. Taylor, Carolyn Webster‐Stratton, Edward G. Feil, Berry Broadbent, Christopher S. Widdop, and Herbert H. Severson. 2008. Computer‐Based Intervention with Coaching: An Example Using the Incredible Years Program. Cognitive Behaviour Therapy 37, 4 (2008), 233–246. https://doi.org/10.1080/16506070802364511Google ScholarCross Ref
- Ashley B. Tempel, Stephanie M. Wagner, and Cheryl B. McNeil. 2013. Behavioral Parent Training Skills and Child Behavior: The Utility of Behavioral Descriptions and Reflections. Child & Family Behavior Therapy 35, 1 (2013), 25–40. https://doi.org/10.1080/07317107.2013.761009Google ScholarCross Ref
- Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, George Karypis, Ehimwenma Nosakhare, and Rosalind Picard. 2019. Probabilistic Latent Variable Modeling for Assessing Behavioral Influences on Well-Being. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019), 2718–2726. https://doi.org/10.1145/3292500.3330738Google ScholarDigital Library
- Nikki Theofanopoulou and Petr Slovak. 2022. Exploring Technology-Mediated Parental Socialisation of Emotion: Leveraging an Embodied, In-situ Intervention for Child Emotion Regulation. CHI Conference on Human Factors in Computing Systems (2022), 1–16. https://doi.org/10.1145/3491102.3502130Google ScholarDigital Library
- Carol M. Trivette, Carl J. Dunst, and Deborah W. Hamby. 2010. Influences of Family-Systems Intervention Practices on Parent-Child Interactions and Child Development. Topics in Early Childhood Special Education 30, 1 (2010), 3–19. https://doi.org/10.1177/0271121410364250Google ScholarCross Ref
- University, Department of Clinical Psychology and Psychotherapy, Babeş-Bolyai, Health", The International Institute for the Advanced Studies of Psychotherapy and Applied Mental, and Oana A DAVID. 2019. "The Rational Parenting Coach app: REThink parenting! A mobile parenting program for offering evidence-based personalized support in the prevention of child externalizing and internalizing disorders". Journal of Evidence-Based Psychotherapies 19, 2 (2019), 97–108. https://doi.org/10.24193/jebp.2019.2.15Google ScholarCross Ref
- George Veletsianos. 2010. Contextually relevant pedagogical agents: Visual appearance, stereotypes, and first impressions and their impact on learning. Computers & Education 55, 2 (2010), 576–585. https://doi.org/10.1016/j.compedu.2010.02.019Google ScholarDigital Library
- Kim Veltman, Harmen de Weerd, and Rineke Verbrugge. 2019. Training the use of theory of mind using artificial agents. Journal on Multimodal User Interfaces 13, 1 (2019), 3–18. https://doi.org/10.1007/s12193-018-0287-xGoogle ScholarCross Ref
- Leena Ventä-Olkkonen, Netta Iivari, Sumita Sharma, Tonja Molin-Juustila, Kari Kuutti, Nina Juustila-Cevirel, Essi Kinnunen, and Jenni Holappa. 2021. Nowhere to Now-here: Empowering Children to Reimagine Bully Prevention at Schools Using Critical Design Fiction: Exploring the Potential of Participatory, Empowering Design Fiction in Collaboration with Children. Designing Interactive Systems Conference 2021 (2021), 734–748. https://doi.org/10.1145/3461778.3462044Google ScholarDigital Library
- Sarah Theres Völkel, Daniel Buschek, Malin Eiband, Benjamin R Cowan, and Heinrich Hussmann. 2021. Eliciting and Analysing Users’ Envisioned Dialogues with Perfect Voice Assistants. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (2021), 1–15. https://doi.org/10.1145/3411764.3445536 arXiv:2102.13508Google ScholarDigital Library
- Junqing Wang, Aisling Ann O’Kane, Nikki Newhouse, Geraint Rhys Sethu-Jones, and Kaya de Barbaro. 2017. Quantified Baby: Parenting and the Use of a Baby Wearable in the Wild. Proceedings of the ACM on Human-Computer Interaction 1, CSCW (2017), 1–19. https://doi.org/10.1145/3134743Google ScholarDigital Library
- Jinping Wang, Hyun Yang, Ruosi Shao, Saeed Abdullah, and S Shyam Sundar. 2020. Alexa as Coach: Leveraging Smart Speakers to Build Social Agents that Reduce Public Speaking Anxiety. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020), 1–13. https://doi.org/10.1145/3313831.3376561Google ScholarDigital Library
- John Ward‐Horner and Peter Sturmey. 2012. COMPONENT ANALYSIS OF BEHAVIOR SKILLS TRAINING IN FUNCTIONAL ANALYSIS. Behavioral Interventions 27, 2 (2012), 75–92. https://doi.org/10.1002/bin.1339Google ScholarCross Ref
- Carolyn Webster-Stratton, M. Jamila Reid, and Mary Hammond. 2004. Treating Children With Early-Onset Conduct Problems: Intervention Outcomes for Parent, Child, and Teacher Training. Journal of Clinical Child & Adolescent Psychology 33, 1 (2004), 105–124. https://doi.org/10.1207/s15374424jccp3301_11Google ScholarCross Ref
- Stephen G. West, Leona S. Aiken, and Michael Todd. 1993. Probing the effects of individual components in multiple component prevention programs. American Journal of Community Psychology 21, 5 (1993), 571–605. https://doi.org/10.1007/bf00942173Google ScholarCross Ref
- Drew Westen, Catherine M. Novotny, and Heather Thompson-Brenner. 2004. The Empirical Status of Empirically Supported Psychotherapies: Assumptions, Findings, and Reporting in Controlled Clinical Trials. Psychological Bulletin 130, 4 (2004), 631–663. https://doi.org/10.1037/0033-2909.130.4.631Google ScholarCross Ref
- Brenda K. Wiederhold. 2018. “Alexa, Are You My Mom?” The Role of Artificial Intelligence in Child Development. Cyberpsychology, Behavior, and Social Networking 21, 8 (2018), 471–472. https://doi.org/10.1089/cyber.2018.29120.bkwGoogle ScholarCross Ref
- Jessica Williams, Stephen M Fiore, and Florian Jentsch. 2021. Supporting Artificial Social Intelligence With Theory of Mind.Frontiers in artificial intelligence 5 (2021), 750763. https://doi.org/10.3389/frai.2022.750763Google ScholarCross Ref
- Terry Winograd. 1972. Understanding natural language. Cognitive Psychology 3, 1 (1972), 1–191. https://doi.org/10.1016/0010-0285(72)90002-3Google ScholarCross Ref
- Leanne Winter, Alina Morawska, and Matthew R. Sanders. 2012. The Effect of Behavioral Family Intervention on Knowledge of Effective Parenting Strategies. Journal of Child and Family Studies 21, 6 (2012), 881–890. https://doi.org/10.1007/s10826-011-9548-yGoogle ScholarCross Ref
- Richmond Y. Wong, Deirdre K. Mulligan, Ellen Van Wyk, James Pierce, and John Chuang. 2017. Eliciting Values Reflections by Engaging Privacy Futures Using Design Workbooks. Proceedings of the ACM on Human-Computer Interaction 1, CSCW (2017), 1–26. https://doi.org/10.1145/3134746Google ScholarDigital Library
- Richmond Y. Wong, Jason Caleb Valdez, Ashten Alexander, Ariel Chiang, Olivia Quesada, and James Pierce. 2023. Broadening Privacy and Surveillance: Eliciting Interconnected Values with a Scenarios Workbook on Smart Home Cameras. Proceedings of the 2023 ACM Designing Interactive Systems Conference (2023), 1093–1113. https://doi.org/10.1145/3563657.3596012Google ScholarDigital Library
- Kieran Woodward, Eiman Kanjo, David J. Brown, T. M. McGinnity, Becky Inkster, Donald J. Macintyre, and Athanasios Tsanas. 2022. Beyond Mobile Apps: A Survey of Technologies for Mental Well-Being. IEEE Transactions on Affective Computing 13, 3 (2022), 1216–1235. https://doi.org/10.1109/taffc.2020.3015018Google ScholarCross Ref
- Ying Xu, Kunlei He, Valery Vigil, Santiago Ojeda-Ramirez, Xuechen Liu, Julian Levine, Kelsyann Cervera, and Mark Warschauer. 2023. “Rosita Reads With My Family”: Developing A Bilingual Conversational Agent to Support Parent-Child Shared Reading. Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (2023), 160–172. https://doi.org/10.1145/3585088.3589354Google ScholarDigital Library
- Sarita Yardi and Amy Bruckman. 2012. Income, race, and class: exploring socioeconomic differences in family technology use. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2012), 3041–3050. https://doi.org/10.1145/2207676.2208716Google ScholarDigital Library
- Chungkuk Yoo, Seungwoo Kang, Inseok Hwang, Chulhong Min, Seonghoon Kim, Wonjung Kim, and Junehwa Song. 2019. Mom, I see You Angry at Me! Designing a Mobile Service for Parent-child Conflicts by In-situ Emotional Empathy. Proceedings of the 5th ACM Workshop on Mobile Systems for Computational Social Science - MCSS ’19 (2019), 21–26. https://doi.org/10.1145/3325426.3329947Google ScholarDigital Library
- Jingwen Zhang, Yoo Jung Oh, Patrick Lange, Zhou Yu, and Yoshimi Fukuoka. 2020. Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint. Journal of Medical Internet Research 22, 9 (2020), e22845. https://doi.org/10.2196/22845Google ScholarCross Ref
- Zheng Zhang, Ying Xu, Yanhao Wang, Bingsheng Yao, Daniel Ritchie, Tongshuang Wu, Mo Yu, Dakuo Wang, and Toby Jia-Jun Li. 2022. StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement. CHI Conference on Human Factors in Computing Systems (2022), 1–21. https://doi.org/10.1145/3491102.3517479 arXiv:2202.06205Google ScholarDigital Library
- Qing Zhou, Nancy Eisenberg, Sandra H. Losoya, Richard A. Fabes, Mark Reiser, Ivanna K. Guthrie, Bridget C. Murphy, Amanda J. Cumberland, and Stephanie A. Shepard. 2002. The Relations of Parental Warmth and Positive Expressiveness to Children’s Empathy‐Related Responding and Social Functioning: A Longitudinal Study. Child Development 73, 3 (2002), 893–915. https://doi.org/10.1111/1467-8624.00446Google ScholarCross Ref
- John Zimmerman and Jodi Forlizzi. 2017. Speed Dating: Providing a Menu of Possible Futures. She Ji: The Journal of Design, Economics, and Innovation 3, 1 (2017), 30–50. https://doi.org/10.1016/j.sheji.2017.08.003Google ScholarCross Ref
Index Terms
- Socio-technical Imaginaries: Envisioning and Understanding AI Parenting Supports through Design Fiction
Recommendations
Understanding Online Parental Help-Seeking and Help-Giving in Early Childhood: The Design Challenges of Supporting Complex Parenting Questions
CSCWEarly parenting is one of the strongest predictors of child well-being. Online social communities have shown promise in supporting parents across a range of contexts. However, we only have a limited understanding of how posters and commenters interact ...
How Do Mothers and Fathers Talk About Parenting to Different Audiences?: Stereotypes and Audience Effects: An Analysis of r/Daddit, r/Mommit, and r/Parenting Using Topic Modelling
WWW '22: Proceedings of the ACM Web Conference 2022While major strides have been made towards gender equality in public life, serious inequality remains in the domestic sphere, especially around parenting. The present study analyses discussions about parenting on Reddit (i.e., a content aggregation ...
Parents, Passwords, and Parenting: How Parents Think about Passwords and are Involved in Their Children’s Password Practices
HCI for Cybersecurity, Privacy and TrustAbstractThough much is known about how adults understand and use passwords, little research attention has been paid specifically to parents or, more importantly, to how parents are involved in their children’s password practices. To better understand both ...
Comments