Abstract
This chapter critically reflects how Artificial Intelligence can be used to foster human learning in intelligent working systems. Rapid technological innovations of the fourth industrial revolution require continuous learning in the workplace, which can be, however, hindered by the innovations itself. Increasing levels of automation and digitalization lead to intelligent working systems that are capable to fulfill most tasks automatically. Human operators must step in only in ill-defined non-routine situations and traditional approaches of workplace learning (e.g., continuous training through own experience) can hardly be applied. However, Artificial Intelligence and further technologies can be used to transform working systems into smart learning environments that still foster workplace learning. This chapter provides an introduction to the technological framework within Industry 4.0 and characterizes cyber-physical production systems with special regard to human learning and performance. The integration of Artificial Intelligence into smart learning environments is discussed and two principles from instructional psychology are presented (i.e., scaffolding and feedback). Further, their possible integration into smart learning environments is illustrated.
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References
Abbass, H. A. (2019). Social integration of artificial intelligence: Functions, automation allocation logic and human-autonomy trust. Cognitive Computation, 11, 159–171. https://doi.org/10.1007/s12559-018-9619-0
Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., & Landis, R. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427–449). Springer. https://doi.org/10.1007/978-1-4419-5546-3_28
Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779.
Bereiter, C., & Scardamalia, M. (2014). Knowledge building and knowledge creation: One concept, two hills to climb. In S. C. Tan, H. J. So, & J. Yeo (Eds.), Knowledge creation in education (pp. 35–52). Springer.
Booth, J. L., McGinn, K. M., Barbieri, C., Begolli, K. N., Chang, B., Miller-Cotto, D., Young, L. K., & Davenport, J. L. (2017). Evidence for cognitive science principles that impact learning in mathematics. In D. C. Geary, D. B. Berch, R. J. Ochsendorf, & K. Mann Koepke (Eds.), Acquisition of complex arithmetic skills and higher-order mathematics concepts (pp. 297–325). Academic. https://doi.org/10.1016/b978-0-12-805086-6.00013-8
Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What can machines learn and what does it mean for occupations and the economy? AEA Papers and Proceedings, 2018(108), 43–47. https://doi.org/10.1257/pandp.20181019
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
Duffy, M. C., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348. https://doi.org/10.1016/j.chb.2015.05.041
Eling, M., & Lehmann, M. (2018). The impact of digitalization on the insurance value chain and the insurability of risks. Geneva Papers on Risk and Insurance: Issues and Practice, 43, 359–396. https://doi.org/10.1057/s41288-017-0073-0
Endsley, M. R., & Kiris, E. O. (1995). The out-of-the-loop performance problem and level of control in automation. Human Factors, 37(2), 381–394. https://doi.org/10.1518/001872095779064555
Ertel, W. (2017). Introduction to artificial intelligence (2nd ed.). Springer International Publishing. https://doi.org/10.1007/978-981-16-2842-9_1
GMA. (2013). Cyber-Physical Systems: Chancen und Nutzen aus Sicht der Automation.
Graesser, A. C., Hu, X., & Sottilare, R. (2018). Intelligent tutoring systems. In International Handbook of the Learning Sciences (pp. 246–255). https://doi.org/10.4324/9781315617572
Gros, B. (2016). The design of smart educational environments. Smart Learning Environments, 3(15). https://doi.org/10.1186/s40561-016-0039-x
Hirsch-Kreinsen, H. (2014). Wandel von Produktionsarbeit – “Industrie 4.0”. WSI Mitteilungen, 6, 421–429.
Holden, H. K., & Sinatra, A. M. (2014). A guide to scaffolding and guided Instrucational strategies for ITSs. In R. A. Sottilare, A. C. Graesser, X. Hu, & B. S. Goldberg (Eds.), Design recommendations for intelligent tutoring systems. Volume 2: Instructional management (pp. 265–281). U.S. Army Research Laboratory.
Huber, W. (2018). Industrie 4.0 kompakt – Wie Technologien unsere Wirtschaft und unsere Unternehmen verändern. Springer. https://doi.org/10.1007/978-3-658-20799-1
Hwang, G.-J. (2014). Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(4), 1–14. https://doi.org/10.1186/s40561-014-0004-5
Ifenthaler, D., & Drachsler, H. (2020). Learning analytics. In H. Niegemann & A. Weinberger (Eds.), Handbuch Bildungstechnologie: Konzeption und Einsatz digitaler Lernumgebungen (pp. 515–534). Springer.
ITU. (2012). Recommendation ITU-T Y.2060. Overview of the Internet of things.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31. https://doi.org/10.1207/S15326985EP3801_4
Kluge, A. (2014). The Acquisition of knowledge and skills for Taskwork and teamwork to control complex technical systems. Springer. https://doi.org/10.1007/978-94-007-5049-4
Koedinger, K. R., & Aleven, V. (2007). Exploring the assistance dilemma in experiments with cognitive tutors. Educational Psychology Review, 19, 239–264. https://doi.org/10.1007/s10648-007-9049-0
Koper, R. (2014). Conditions for effective smart learning environments. Smart Learning Environments, 1(5), 1–17. https://doi.org/10.1186/s40561-014-0005-4
Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business and information. Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334-4
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001
Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Applications, 23, 368–375. https://doi.org/10.1007/s11036-017-0932-8
Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1(28), 1–3. https://doi.org/10.1038/s41562-016-0028
Mangaroska, K., & Giannakos, M. (2019). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516–534. https://doi.org/10.1109/TLT.2018.2868673
Manuti, A., Pastore, S., Scardigno, A. F., Giancaspro, M. L., & Morciano, D. (2015). Formal and informal learning in the workplace: A research review. International Journal of Training and Development, 19(1), 1–17. https://doi.org/10.1111/ijtd.12044
Martin, N. D., Dornfeld Tissenbaum, C., Gnesdilow, D., & Puntambekar, S. (2019). Fading distributed scaffolds: The importance of complementarity between teacher and material scaffolds. Instructional Science, 47(1), 69–98. https://doi.org/10.1007/s11251-018-9474-0
Mathan, S. A., & Koedinger, K. R. (2005). Fostering the intelligent novice: Learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257–265. https://doi.org/10.4324/9781315866239-7
Mousavinasab, E., Zarifsanaiey, N., Niakan Kalhori, R., & S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021). Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1), 142–163. https://doi.org/10.1080/10494820.2018.1558257
Müller, R. (2019). Cognitive challenges of changeability: Adjustment to system changes and transfer of knowledge in modular chemical plants. Cognition, Technology & Work, 21(1), 113–131. https://doi.org/10.1007/s10111-018-0489-8
Müller, R., Narciss, S., & Urbas, L. (2017). Interfacing cyber-physical production systems with human decision makers. In H. Song, D. B. Rawar, S. Jeschke, & C. Brecher (Eds.), Cyber-physical systems (pp. 145–160). Academic. https://doi.org/10.1016/B978-0-12-803801-7.00010-9
Müller, R., Kessler, F., Humphrey, D. W., & Rahm, J. (2021). Data in context: How digital transformation can support human reasoning in cyber-physical production systems. Future Internet, 13, 1–36. https://doi.org/10.3390/fi13060156
Najar, A. S., Mitrovic, A., & McLaren, B. M. (2016). Learning with intelligent tutors and worked examples: Selecting learning activities adaptively leads to better learning outcomes than a fixed curriculum. User Modeling and User-Adapted Interaction, 26(5), 459–491. https://doi.org/10.1007/s11257-016-9181-y
Narciss, S. (2008). Feedback strategies for interactive learning tasks. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 125–143). Routledge. https://doi.org/10.1007/978-1-4419-1428-6
Narciss, S. (2012). Feedback strategies. In N. Seel (Ed.), Encyclopedia of the learning sciences (pp. 1289–1293). Springer.
Narciss, S. (2013). Designing and evaluating tutoring feedback strategies for digital learning. Digital Education Review, 23, 7–26. https://doi.org/10.1109/ICALT.2011.157
Narciss, S., Sosnovsky, S., Schnaubert, L., Andrès, E., Eichelmann, A., Goguadze, G., & Melis, E. (2014). Exploring feedback and student characteristics relevant for personalizing feedback strategies. Computers and Education, 71, 56–76. https://doi.org/10.1016/j.compedu.2013.09.011
Noroozi, O., Kirschner, P. A., Biemans, H. J. A., & Mulder, M. (2018). Promoting argumentation competence: Extending from first- to second-order scaffolding through adaptive fading. Educational Psychology Review, 30, 153–176. https://doi.org/10.1007/s10648-017-9400-z
Nye, B. D., Boyce, M. W., & Sottilare, R. A. (2016). Defining the ill-defined: From abstract principles to applied pedagogy. In R. A. Sottilare, A. C. Graesser, X. Hu, A. M. Olney, B. D. Nye, & A. M. Sinatra (Eds.), Design recommendations for intelligent tutoring systems. Volume 4: Domain modeling (pp. 19–37). U.S. Army Research Laboratory.
Pan, Y. (2016). Heading toward artificial intelligence 2.0. Engineering, 2, 409–413. https://doi.org/10.1016/J.ENG.2016.04.018
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans., 30(3), 286–297. https://doi.org/10.1109/3468.844354
Pavlik, P. I., Brawner, K., Olney, A., & Mitrovic, A. (2013). A review of student models used in intelligent tutoring systems. In R. Sottilare, A. Graesser, X. Hu, & H. Holden (Eds.), Design recommendations for intelligent tutoring systems. Volume 1: Learner modeling (pp. 39–67). U.S. Army Research Laboratory.
Pereira, A. C., & Romero, F. (2017). A review of the meanings and the implications of the industry 4.0 concept. Procedia Manufacturing, 13, 1206–1214. https://doi.org/10.1016/j.promfg.2017.09.013
Radanliev, P., De Roure, D., Van Kleek, M., Santos, O., & Ani, U. (2021). Artificial intelligence in cyber physical systems. AI and Society, 36, 783–796. https://doi.org/10.1007/s00146-020-01049-0
Reiser, B. J., & Tabak, I. (2014). Scaffolding. In The Cambridge handbook of the learning sciences (pp. 44–62). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.005
Rieth, M., & Hagemann, V. (2022). Automation as an equal team player for humans? – A view into the field and implications for research and practice. Applied Ergonomics, 98. https://doi.org/10.1016/j.apergo.2021.103552
Romero, D., Stahre, J., Wuest, T., Noran, O., Bernus, P., Fast-Berglund, Å., & Gorecky, D. (2016). Towards an operator 4.0 typology: A human-centric perspective on the fourth industrial revolution technologies. In Proceedings of the 46th International Conferences on Computers and Industrial Engineering (CIE46).
Ruppert, T., Jaskó, S., Holczinger, T., & Abonyi, J. (2018). Enabling Technologies for Operator 4.0: A survey. Applied Sciences, 8(9). https://doi.org/10.3390/app8091650
Spector, J. M. (2014). Conceptualizing the emerging field of smart learning environments. Smart Learning Environments, 1(2), 1–10. https://doi.org/10.1186/s40561-014-0002-7
Spector, J. M. (2016). Smart learning environments: Concepts and issues. In G. Chamblee & L. Langub (Eds.), SITE 2016 - 27th International Conference of the Society for Information Technology and Teacher Education (Vol. 2016(1), pp. 2728–2737). Association for the Advancement of Computing in Education (AACE).
Thorvald, P., Fast Berglund, Å., & Romero, D. (2021). The cognitive operator 4.0. Advances in Transdisciplinary Engineering, 15, 3–8. https://doi.org/10.3233/ATDE210003
Tvenge, N., & Martinsen, K. (2018). Integration of digital learning in industry 4.0. Procedia Manufacturing, 23, 261–266. https://doi.org/10.1016/j.promfg.2018.04.027
Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 - a glimpse. Procedia Manufacturing, 20, 233–238. https://doi.org/10.1016/j.promfg.2018.02.034
van Merriënboer, J. J. G., Clark, R. E., & de Croock, M. B. M. (2002). Blueprints for complex learning: The 4C/ID-model. Educational Technology Research and Development, 50(2), 39–64.
Vannaprathip, N., Haddawy, P., Schultheis, H., & Suebnukarn, S. (2021). Intelligent tutoring for surgical decision making: A planning-based approach. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-021-00261-3
Wang, D., Han, H., Zhan, Z., Xu, J., Liu, Q., & Ren, G. (2015a). A problem solving oriented intelligent tutoring system to improve students’ acquisition of basic computer skills. Computers and Education, 81, 102–112. https://doi.org/10.1016/j.compedu.2014.10.003
Wang, L., Törngren, M., & Onori, M. (2015b). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517–527. https://doi.org/10.1016/j.jmsy.2015.04.008
Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10(3087), 1–14. https://doi.org/10.3389/fpsyg.2019.03087
Wortmann, F., & Flüchter, K. (2015). Internet of things: Technology and value added. Business and Information Systems Engineering, 57(3), 221–224. https://doi.org/10.1007/s12599-015-0383-3
Wu, L. (2020). Student model construction of intelligent teaching system based on Bayesian network. Personal and Ubiquitous Computing, 24, 419–428. https://doi.org/10.1007/s007799-019-01311-3
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
Zhu, Z. T., Yu, M. H., & Riezebos, P. (2016). A research framework of smart education. Smart Learning Environments, 3(4), 1–17. https://doi.org/10.1186/s40561-016-0026-2
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Miesen, F., Narciss, S. (2022). Workplace Learning in and with Intelligent Systems. In: Ifenthaler, D., Seufert, S. (eds) Artificial Intelligence Education in the Context of Work. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-031-14489-9_11
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