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An in-depth analysis of humanoid robotics in higher education system

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Abstract

Humanoid robotics (HR) studies around the world are on the rise today. As a result of its enormous contributions to enhancing productivity and efficiency in education and other spheres of life brought the attention of it to the academic community to consider it as an adequate learning tool in higher education. However, the study revealed that African society and higher education have a limited understanding of humanoid robots. In order to track the trends of humanoid robotics (HR) in higher education, a systematic review and a few crucial bibliometrics elements approach were adopted, and the published documents from the Web of Science and Scopus databases from 2000–2022 were used in the analysis. HR publications in higher education within this period revealed that Japan, the USA, and China are the leading countries in the application of humanoid robots both in the industrial and educational sectors. Furthermore, HR is a new phenomenon in the African educational community, and South Africa remains the singular African nation that has worked in this domain by collaborating with other developed countries. This current research discovered the inexistence of HR in the African educational environment and therefore suggests that African educational policymakers and education curriculum developers should endeavor to implement educational policies that will support the institutionalization of HR in the teaching and learning activities in African higher education.

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References

  • Akintunde, T. Y., Musa, T. H., Musa, H. H., Musa, I. H., Shaojun, C., Ibrahim, E., … Helmy, M. S. E. D. M. (2021). Bibliometric Analysis of Global Scientific Literature on Effects of COVID-19 Pandemic on Mental Health. Asian Journal of Psychiatry, 102753. https://doi.org/10.1016/J.AJP.2021.102753

  • Alemi, M., Meghdari, A., & Ghazisaedy, M. (2015). The impact of social robotics on L2 learners’ anxiety and attitude in english vocabulary acquisition. International Journal of Social Robotics, 7(4), 523–535. https://doi.org/10.1007/s12369-015-0286-y

    Article  Google Scholar 

  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics. https://doi.org/10.1016/j.joi.2017.08.007

    Article  Google Scholar 

  • Asongu, S., & Odhiambo, N. (2019). Enhancing ICT for quality education in sub-saharan Africa. SSRN Electronic Journal, August. https://doi.org/10.2139/ssrn.3328012

    Article  Google Scholar 

  • Baker, R. S., & Ga, D. (2021). Computers and Education: Artificial Intelligence Four paradigms in learning analytics: Why paradigm convergence matters. 2(April). https://doi.org/10.1016/j.caeai.2021.100021

  • Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3(21), eaat5954. https://doi.org/10.1126/scirobotics.aat5954

    Article  Google Scholar 

  • Berenz, V., & Suzuki, K. (2014). Targets-Drives-Means: A declarative approach to dynamic behavior specification with higher usability. Robotics and Autonomous Systems, 62(4), 545–555. https://doi.org/10.1016/j.robot.2013.12.010

    Article  Google Scholar 

  • Bhounsule, P. A., Cortell, J., Grewal, A., Hendriksen, B., Daniël Karssen, J. G., Paul, C., & Ruina, A. (2014). Low-bandwidth reflex-based control for lower power walking: 65 km on a single battery charge. International Journal of Robotics Research, 33(10), 1305–1321. https://doi.org/10.1177/0278364914527485

    Article  Google Scholar 

  • Campbell, J. C., Hindle, A., & Stroulia, E. (2015). Latent dirichlet allocation: Extracting topics from software engineering data. Elsevier Inc. https://doi.org/10.1016/B978-0-12-411519-4.00006-9

    Book  Google Scholar 

  • Causo, A., Vo, G. T., Chen, I.-M., & Yeo, S. H. (2016). Design of robots used as education companions and tutors. In S. Zeghloul, M. A. Laribi, & J.-P. Gazeau (Eds.), Robotics and Mechatronics (pp. 75–84). Springer International Publishing. https://doi.org/10.1007/978-3-319-22368-1_8

  • Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y. S., Gašević, D., & Mello, R. F. (2021). Automatic feedback in online learning environments: A systematic literature review. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100027

  • Chalmers, C., Keane, T., Boden, M., & Williams, M. (2022). Humanoid robots go to school. Education and Information Technologies, 27(6), 7563–7581. https://doi.org/10.1007/s10639-022-10913-z

    Article  Google Scholar 

  • Chassignol, M., Khoroshavin, A., Klimova, A., Bilyatdinova, A., Chassignol, M., Khoroshavin, A., & Klimova, A. (2018). Science direct artificial intelligence trends in conference education: A narrative overview artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233

    Article  Google Scholar 

  • Cherubini, A., Giannone, F., Iocchi, L., Lombardo, M., & Oriolo, G. (2009). Policy gradient learning for a humanoid soccer robot. Robotics and Autonomous Systems, 57(8), 808–818. https://doi.org/10.1016/j.robot.2009.03.006

    Article  Google Scholar 

  • Chin, K. Y., Wu, C. H., & Hong, Z. W. (2011). A humanoid robot as a teaching assistant for primary education. Proceedings - 2011 5th International Conference on Genetic and Evolutionary Computing, ICGEC 2011, 21–24. https://doi.org/10.1109/ICGEC.2011.13

  • Commission, E. (2022). The AI, Data, and Robotics. In Adr-Association of EU.

  • Denny, J., Elyas, M., D, S. A., & Souza, R. D. D. (2016). Humanoid Robots – Past, Present, and the Future. June.

  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Marc, W. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(March), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

    Article  Google Scholar 

  • Ekström, S., & Pareto, L. (2022). The dual role of humanoid robots in education: As didactic tools and social actors. In Education and Information Technologies: Springer, US. https://doi.org/10.1007/s10639-022-11132-2

    Book  Google Scholar 

  • Feng, L., & Chen, Q. (2020). Bibliometric analysis of the synthesis of nanocatalyst (1999–2018). IOP Conference Series: Earth and Environmental Science, 558(4), 042042. https://doi.org/10.1088/1755-1315/558/4/042042

    Article  Google Scholar 

  • Feng, Y., Barakova, E. I., Yu, S. H., Hu, J., & Rauterberg, G. W. M. (2020). Effects of the level of interactivity of a social robot and the response of the augmented reality display in contextual interactions of people with dementia. SENSORS, 20(13). https://doi.org/10.3390/s20133771

  • Francis, E., Perpetua, U., Yinka, T., Bala, M., Nchekwubemchukwu, S., Modest, K., & Ouattara, T. (2023a). Social sciences & humanities open a comprehensive overview of artificial intelligence and machine learning in education pedagogy : 21 Years (2000–2021) of research indexed in the Scopus database. Social Sciences & Humanities Open, 8(1), 100655. https://doi.org/10.1016/j.ssaho.2023.100655

    Article  Google Scholar 

  • Francis, E., Perpetua, U., Nchekwubemchukwu, S., Emeka, I., Kosiso, O., Tidiane, A., & Onyinye, E. (2023). International Journal of Educational Research Open The effects of the COVID-19 pandemic on the education system in Nigeria : The role of competency-based education. International Journal of Educational Research Open, 4(August 2022), 100219. https://doi.org/10.1016/j.ijedro.2022.100219

    Article  Google Scholar 

  • Fridin, M., & Belokopytov, M. (2014). Acceptance of socially assistive humanoid robots by preschool and elementary school teachers. Computers in Human Behavior, 33, 23–31. https://doi.org/10.1016/j.chb.2013.12.016

    Article  Google Scholar 

  • Garg, K. C., & Bebi. (2021). COLLNET Journal of Scientometrics and Information Management: A bibliometric study. COLLNET Journal of Scientometrics and Information Management, 15(1), 47–61. https://doi.org/10.1080/09737766.2021.1920067

    Article  Google Scholar 

  • Garner, J. R., Smart, W. D., Bennett, K., Bruemmer, D. J., Few, D. A., & Roman, C. M. (2004). The remote exploration program: A collaborative outreach approach to robotics education. Proceedings - IEEE International Conference on Robotics and Automation, 2004(2), 1826–1830. https://doi.org/10.1109/robot.2004.1308089

    Article  Google Scholar 

  • Guggemos, J., Seufert, S., & Sonderegger, S. (2020). Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT. British Journal of Educational Technology, 51(5), 1864–1883. https://doi.org/10.1111/bjet.13006

    Article  Google Scholar 

  • Huang, S. H. (2015). Supervised feature selection: A tutorial. Artificial Intelligence Research, 4(2). https://doi.org/10.5430/air.v4n2p22

  • Ince, G., Yorganci, R., Ozkul, A., Duman, T. B., & Köse, H. (2021). An audiovisual interface-based drumming system for multimodal human–robot interaction. Journal on Multimodal User Interfaces, 15(4), 413–428. https://doi.org/10.1007/s12193-020-00352-w

    Article  Google Scholar 

  • Ioannou, A., & Makridou, E. (2018). Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work. Education and Information Technologies, 23(6), 2531–2544. https://doi.org/10.1007/s10639-018-9729-z

    Article  Google Scholar 

  • Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2(March), 100017. https://doi.org/10.1016/j.caeai.2021.100017

    Article  Google Scholar 

  • Kaffash, S., Nguyen, A. T., & Zhu, J. (2021). Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis. International Journal of Production Economics, 231(December 2019), 107868. https://doi.org/10.1016/j.ijpe.2020.107868

    Article  Google Scholar 

  • Keane, T., Chalmers, C., Boden, M., & Williams, M. (2019). Humanoid robots: Learning a programming language to learn a traditional language. Technology, Pedagogy, and Education, 28(5), 533–546. https://doi.org/10.1080/1475939X.2019.1670248

    Article  Google Scholar 

  • Khairy, D., Alkhalaf, S., Areed, M. F., Amasha, M. A., & Abougalala, R. A. (2022). An algorithm for providing adaptive behavior to humanoid robot in oral assessment. International Journal of Advanced Computer Science and Applications, 13(9), 933–939. https://doi.org/10.14569/IJACSA.2022.01309119

    Article  Google Scholar 

  • Khanlari, A. (2019). Knowledge Building in Robotics for Math Education.

  • Kim, C., Kim, D., Yuan, J., Hill, R. B., Doshi, P., & Thai, C. N. (2015). Robotics to promote elementary education pre-service teachers’ STEM engagement, learning, and teaching. Computers and Education, 91, 14–31. https://doi.org/10.1016/j.compedu.2015.08.005

    Article  Google Scholar 

  • Kim, C. M., Yuan, J., Kim, D., Doshi, P., Thai, C. N., Hill, R. B., & Melias, E. (2019). Studying the usability of an intervention to promote teachers’ use of robotics in STEM education. Journal of Educational Computing Research, 56(8), 1179–1212. https://doi.org/10.1177/0735633117738537

    Article  Google Scholar 

  • Kory, J., & Breazeal, C. (2014). Storytelling with robots: Learning companions for preschool children’s language development. The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 643–648. https://doi.org/10.1109/ROMAN.2014.6926325

  • Kumar, S., Giagkos, A., Shaw, P., Braud, R., Lee, M., & Shen, Q. (2022). Discovering schema-based action sequences through play in situated humanoid robots. IEEE Transactions on Cognitive and Developmental Systems, 14(3), 1021–1035. https://doi.org/10.1109/TCDS.2021.3094513

    Article  Google Scholar 

  • Kumazaki, H., Warren, Z., Muramatsu, T., Yoshikawa, Y., Matsumoto, Y., Miyao, M., Nakano, M., Mizushima, S., Wakita, Y., Ishiguro, H., Mimura, M., Minabe, Y., & Kikuchi, M. (2017). A pilot study for robot appearance preferences among high-functioning individuals with autism spectrum disorder: Implications for therapeutic use. 1–13.

  • Lee, H. S., & Lee, J. (2021). Applying artificial intelligence in physical education and future perspectives. Sustainability (switzerland), 13(1), 1–16. https://doi.org/10.3390/su13010351

    Article  Google Scholar 

  • Levinson, L., Gvirsman, O., Gorodesky, I. M., Perez, A., Gonen, E., & Gordon, G. (2021). Learning in summer camp with social robots: A morphological study studying dynamics using social robots. International Journal of Social Robotics, 13(5), 999–1012. https://doi.org/10.1007/s12369-020-00689-y

    Article  Google Scholar 

  • Leyzberg, D., Spaulding, S., & Scassellati, B. (2014). Personalizing robot tutors to individuals’ learning differences. 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 423–430.

  • Lilingling. (2021). Online mental health education teaching mode and empirical research based on Artificial intelligence. Journal of Intelligent and Fuzzy Systems, 40(2), 3467–3476. https://doi.org/10.3233/JIFS-189384

    Article  Google Scholar 

  • Liu, M., Zhao, Z., Zhang, W., & Hao, L. (2021). Reinforcement learning control of a humanoid robotic hand actuated by shape memory alloy. Proceedings of the Institution of Mechanical Engineers, Part c: Journal of Mechanical Engineering Science, 235(21), 5736–5744. https://doi.org/10.1177/0954406220982019

    Article  Google Scholar 

  • Ma, W., Kofi Alimo, P., Wang, L., & Abdel-Aty, M. (2022). Mapping pedestrian safety studies between 2010 and 2021: A scientometric analysis. Accident Analysis and Prevention, 174(June), 106744. https://doi.org/10.1016/j.aap.2022.106744

    Article  Google Scholar 

  • Manseur, R. (2016). Software - Aided robotics education and design. IEEE Global Engineering Education Conference, EDUCON, 10–13-Apri(April), 1028–1033. https://doi.org/10.1109/EDUCON.2016.7474679

  • McVey, Sarah-May; Chew, Esyin; Caroll, F. (2021). The review of dyslexic humanoid robotics for reinforcement learning. Proceedings of the European Conference on E-Learning, ECEL, 654–657. https://doi.org/10.34190/EEL.251.132

  • Merlo-Espino, R. D., Villareal-Rodgríguez, M., Morita-Aleander, A., Rodríguez-Reséndiz, J., Pérez-Soto, G. I., & Camarillo-Gómez, K. A. (2018, September). Educational Robotics and Its Impact on the Development of Critical Thinking in Higher Education Students. In 2018 XX Congreso Mexicano de Robótica (COMRob) (pp. 1–4). IEEE.

  • Morita, A., Rodriguez, J., & Engineers, E. (2018). Educational Robotics and its Impact on the Development of critical thinking in higher education. September. https://doi.org/10.1109/COMROB.2018.8689122

  • Muniasamy, A., & Alasiry, A. (2020). Deep learning: The impact on future eLearning. International Journal of Emerging Technologies in Learning, 15(1), 188–199. https://doi.org/10.3991/IJET.V15I01.11435

  • Musa, I. H., Afolabi, L., Musa, T. H., & Musa, H. H. (2022). Artificial Intelligence and Machine Learning in Cancer Research : A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database Artificial Intelligence and Machine Learning in Cancer Research : A Systematic and Thematic Ana. (April). https://doi.org/10.1177/10732748221095946

  • Obaid, M., Aylett, R., Barendregt, W., Basedow, C., Corrigan, L. J., Hall, L., Castellano, G. (2018). Endowing a robotic tutor with empathic qualities: Design and pilot evaluation. International Journal of Humanoid Robotics, 15(6), 1–29. https://doi.org/10.1142/S0219843618500251

  • Okamura, E., & Tanaka, F. (2020). Deployment of a Social Robot into a Classroom of Remote Teaching by Elderly People to School Children: A Case Report. 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020, 626–633. https://doi.org/10.1109/RO-MAN47096.2020.9223499

  • Okuda, M., Takahashi, Y., & Tsuichihara, S. (2022). Human response to humanoid robot that responds to social touch. Applied Sciences-Basel, 12(18), 9193. https://doi.org/10.3390/app12189193

    Article  Google Scholar 

  • Palanica, A., Thommandram, A., & Fossat, Y. (2019). Adult verbal comprehension performance is better from human speakers than social robots, but only for Easy Questions. International Journal of Social Robotics, 11(2), 359–369. https://doi.org/10.1007/s12369-018-0504-5

    Article  Google Scholar 

  • Pirri, S., Lorenzoni, V., & Turchetti, G. (2020). A scoping review and bibliometric analysis of big data applications for Medication adherence: An explorative methodological study to enhance consistency in literature. BMC Health Services Research, 20(1), 1–23. https://doi.org/10.1186/s12913-020-05544-4

    Article  Google Scholar 

  • Pöhner, N., & Hennecke, M. (2018). Evaluation of a robotics course with the humanoid Robot NAO in CS teacher education. ACM International Conference Proceeding Series, 2–3. https://doi.org/10.1145/3265757.3265786

  • Raju, I. P. (n.d.). A Brief Review of Recent Advancement in Humanoid Robotics Research Page No: 3743. IX(Vi), 3743–3748.

  • Robins, B., Dautenhahn, K., Boekhorst, R. T., & Billard, A. (2005). Robotic assistants in therapy and education of children with autism: Can a small humanoid robot help encourage social interaction skills? Universal Access in the Information Society, 4(2), 105–120. https://doi.org/10.1007/s10209-005-0116-3

    Article  Google Scholar 

  • Schöpping, T., Korthals, T., Hesse, M., & Rückert, U. (2019). AMiRo: A mini robot as a versatile teaching platform. Advances in Intelligent Systems and Computing, 829, 177–188. https://doi.org/10.1007/978-3-319-97085-1_18

    Article  Google Scholar 

  • Sergeyev, A., Kinney, M. B., Kuhl, S. A., Alaraje, N., Highum, M., & Mehandiratta, P. (2019, June). University, Community College, and Industry Partnership: Revamping Robotics Education to Meet 21st Century Workforce Needs–NSF-sponsored Project Final Report. In 2019 ASEE Annual Conference & Exposition.

  • Serholt, S., Basedow, C. A., Barendregt, W., & Obaid, M. (2014). Comparing a humanoid tutor to a human tutor delivering an instructional task to children. IEEE-RAS International Conference on Humanoid Robots, 2014, 1134–1141. https://doi.org/10.1109/HUMANOIDS.2014.7041511

    Article  Google Scholar 

  • Siciliano, B., & Khatib, O. (n.d.). Humanoid Robots: Historical perspective, overview, and scope. 3–8.

  • Song, P., & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473–486. https://doi.org/10.1007/s12564-020-09640-2

  • Sun, L., & Yin, Y. (2017). Discovering themes and trends in transportation research using topic modeling. Transportation Research Part c: Emerging Technologies, 77, 49–66. https://doi.org/10.1016/j.trc.2017.01.013

    Article  Google Scholar 

  • Suzuki, K., & Kanoh, M. (2017). Investigating the effectiveness of an expression education support robot that nods and gives hints. Journal of Advanced Computational Intelligence and Intelligent Informatics, 21(3), 483–495. https://doi.org/10.20965/jaciii.2017.p0483

    Article  Google Scholar 

  • Takahashi, Y., Suzuki, T., Hisamitsu, S., Matsuo, Y., Yamawaki, S., & Isonuma, S. (2004, November). Simple humanoid robot for university education. In 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004 (Vol. 1, pp. 146–151). IEEE.

  • Tapus, A., Peca, A., Aly, A., Pop, C., Jisa, L., Pintea, S., Rusu, A. S., & David, D. O. (2012). Children with autism social engagement in interaction with Nao, an imitative robot: A series of single case experiments. Interaction Studies - Social Behaviour and Communication in Biological and Artificial Systems, 13(3), 315–347. https://doi.org/10.1075/is.13.3.01tap

    Article  Google Scholar 

  • Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching Machine Learning in K – 12 Classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9(August), 110558–110572. https://doi.org/10.1109/ACCESS.2021.3097962

    Article  Google Scholar 

  • Tejada, S., Cristina, A., Goodwyne, P., Normand, E., O’Hara, R., & Tarapore, S. (2004a). Virtual synergy: A human-robot interface for urban search and rescue. In AAAI Mobile Robot Competition 2003, Papers from the AAAI Workshop (pp. 13–19). Stanford, CA: AAAI Press.

  • Tejada, S., Cristina, A., Hara, R. O., & Tarapore, S. (2004b). Using virtual synergy for artificial intelligence and robotics education. In AAAI Spring Symposium on Accessible Hands-on Artificial Intelligence and Robotics Education. Stanford, CA: AAAI Press.

  • Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850

  • van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

    Article  Google Scholar 

  • Vittori, A., Cascella, M., Leonardi, M., Monaco, F., Nocerino, D., Cuomo, A., Ottaiano, A., Perri, F., Mascilini, I., Francia, E., Petrucci, E., Marinangeli, F., & Picardo, S. G. (2022). VOSviewer-based bibliometric network analysis for evaluating research on Juvenile Primary Fibromyalgia Syndrome (JPFS). Children, 9(5), 1–8. https://doi.org/10.3390/children9050637

    Article  Google Scholar 

  • Wanichsan, D., Panjaburee, P., & Chookaew, S. (2021). Computers and Education : Artificial Intelligence Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems. Computers and Education: Artificial Intelligence, 2, 100013. https://doi.org/10.1016/j.caeai.2021.100013

  • Warburton, K. (2003). Deep learning and education for sustainability. International Journal of Sustainability in Higher Education, 4(1), 44–56. https://doi.org/10.1108/14676370310455332

    Article  Google Scholar 

  • Weng, C., & Tang, Y. (2014). Computers & Education The relationship between technology leadership strategies and effectiveness of school administration: An empirical study. Computers & Education, 76, 91–107. https://doi.org/10.1016/j.compedu.2014.03.010

    Article  Google Scholar 

  • Wilkerson, S. A., Forsyth, J., & Korpela, C. M. (2017, June). Project-based learning using the robotic operating system (ROS) for undergraduate research applications. In 2017 ASEE Annual Conference & Exposition.

  • Yoshida, E. (n.d.). Humanoid Robots.

  • Yusuf, M. O., & Yusuf, H. T. (2009). Educational reforms in Nigeria: The potentials of information and communication technology (ICT). Educational Research and Reviews, 4(5), 225–230.

    Google Scholar 

  • Zhou, M., Dzingirai, C., Hove, K., Chitata, T., & Mugandani, R. (2022). Adoption, use, and enhancement of virtual learning during COVID-19. Education and Information Technologies, 27(7), 8939–8959. https://doi.org/10.1007/s10639-022-10985-x

    Article  Google Scholar 

  • Zhou, H., Yuen, T. T., Popescu, C., Guillen, A., & Davis, D. G. (2015). Designing teacher professional development workshops for robotics integration across the elementary and secondary school curricula. Proceedings - 2015 International Conference on Learning and Teaching in Computing and Engineering, LaTiCE 2015, 215–216. https://doi.org/10.1109/LaTiCE.2015.21

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Acknowledgements

The authors acknowledged the supports and motivation by the leaders of the African Academic Doctors (OAAD) in ensuring that this novel study is accomplished for the African academic society

Funding

The authors declared no financial funding for this study.

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Authors and Affiliations

  1. Faculty of Education, Southwest University, Chongqing, China

    Ekene Francis Okagbue & Nweze Chiamaka Nneoma

  2. Department of Science Technology and Policy, University of Science and Technology of China, , Anhui-Hefei, 230026, China

    Sayibu Muhideen

  3. Faculty of Education, Educational Leadership and Management, Southwest University, Beibei, Chongqing, 400700, China

    Abazie Genevive Anulika

  4. Faculty of Education, Comparative Education, Southwest University, Chongqing, China

    Ilokanulo Samuel Nchekwubemchukwu

  5. Faculty of Education, Teacher Education, Shanghai Normal University, Shanghai, China

    Onwubuya Gift Chinemerem

  6. College of International Studies, Southwest University, Chongqing, China

    Mustapha Bala Tsakuwa

  7. School of Politics and Public Administration, Zhengzhou University, 100 Science Avenue, Zhongyuan District, Zhengzhou, Henan Province, China

    Lydia Osarfo Achaa

  8. Smart Learning Institute, Beijing Normal University, Smart Learning Institute of Beijing Normal University, 12F, Block A, Jingshi Technology Building, No. 12 Xueyuan South Road, Haidian District, Beijing, 100082, China

    Michael Agyemang Adarkwah

  9. College of International Education, Shanghai University of Medicine and Health Sciences, Shanghai, China

    Komolafe Blessing Funmi

  10. Organization of African Academic Doctors (OAAD), Off Kamiti Road, P.O. Box, 25305000100, Nairobi, Kenya

    Christine Mwase

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Contributions

Ekene Francis Okagbue, Sayibu Muhideen, Abazie Genevive Anulika, Ilokanulo Samuel Nchekwubemchukwu, Mustapha Bala Tsakuwa: Project administration, writing original draft, visualization, conceptualization, data curation, analysis, methodology.

Michael Agyemang Adarkwah, Onwubuya Gift Chinemerem, Lydia Osarfo Achaa, Christine Mwase, Komolafe Blessing, and Nweze Chiamaka Nneoma: Writing original draft, reviewing, editing, visualization.

Corresponding author

Correspondence to Ekene Francis Okagbue.

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The authors agreed in unison, and there is no competing interest whatsoever in this study.

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Okagbue, E.F., Muhideen, S., Anulika, A.G. et al. An in-depth analysis of humanoid robotics in higher education system. Educ Inf Technol 29, 185–217 (2024). https://doi.org/10.1007/s10639-023-12263-w

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