Foundations for Post-Disciplinary and Transdisciplinary Education for Next-Generation Cyber-Physical Systems
DOI:
https://doi.org/10.63385/ipt.v2i3.502Keywords:
Fundamental Concepts, Engineering Education, Cyber-Physical Systems, Next-Generation Systems, Artificial Intelligence, System ManifestationsAbstract
Post-disciplinary and transdisciplinary knowledge synthesis facilitates extensive intellectualization, socialization, personalization, and naturalization of next-generation cyber-physical systems (NG-CPSs). However, it makes learning and teaching NG-CPSs more challenging and calls for new educational methods. These challenges arise because (i) conceptual familiarization with these systems must start in basic education for attitudinal reasons, (ii) proficiency in technologies and system development must continue through undergraduate, graduate, and postgraduate levels, and (iii) keeping pace with the evolution of NG-CPSs requires lifelong learning skills and efforts. The goal of the research was to critically review the current state of the art, reveal the related epistemological and computational issues, examine pedagogical and andragogical concepts, and propose specific approaches based on innovative mental and procedural models. A thorough and systematic literature review was conducted, guided by five main research questions. After clarifying the specific objectives, this treatise, rendered as an argumentative position paper, offers a notional clarification to support a consistent interpretation of foundational concepts and definitions, and their relationships. This is necessary due to the prevailing confusing terminology and frequent differences in concept interpretation. The core features and manifestations of the system paradigm of CPSs are analyzed, and a conceptual model illustrating the strands of paradigmatic evolution of CPSs is proposed. Finally, an overview of the likely operations and characteristics of NG-CPSs is provided. The knowledge components mentioned above are vital not only for informing readers about the state of the art in CPSs but also for establishing a solid foundation for pedagogical advancements.
References
[1] Liu, Y., Peng, Y., Wang, B., et al., 2017. Review on cyber-physical systems. IEEE/CAA Journal of Automatica Sinica. 4(1), 27–40. DOI: https://doi.org/10.1109/JAS.2017.7510349
[2] Gunes, V., Peter, S., Givargis, T., et al., 2014. A survey on concepts, applications, and challenges in cyber-physical systems. KSII Transactions on Internet and Information Systems. 8(12), 4242–4268. DOI: https://doi.org/10.3837/tiis.2014.12.001
[3] Horváth, I., 2012. Beyond advanced mechatronics: New design challenges of social-cyber-physical systems. In Proceedings of the 1st Workshop on Mechatronic Design, Linz, Austria, 30 November 2012; pp. 1–20.
[4] Ragadhita, R., Fiandini, M., Al Husaeni, D.N., et al., 2026. Sustainable development goals (SDGs) in engineering education: Definitions, research trends, bibliometric insights, and strategic approaches. Indonesian Journal of Science and Technology. 11(1), 1–26. DOI: https://doi.org/10.17509/ijost.v11i1.86298
[5] Ani, U.D., Al‐Mhiqani, M., Tuptuk, N., et al., 2025. Socio‐technical security modelling and simulations in cyber‐physical systems: Outlook on knowledge, perceptions, practices, enablers, and barriers. IET Cyber‐Physical Systems: Theory & Applications. 10, e70017. DOI: https://doi.org/10.1049/cps2.70017
[6] Wentzel, A., 2017. A Guide to Argumentative Research Writing and Thinking: Overcoming Challenges. Routledge: New York, NY, USA. DOI: https://doi.org/10.4324/9781315175676
[7] Horváth, I., Ábrahám, G., 2025. Transdisciplinary shifts in system paradigm-driven disciplines: Mechatronics as an example. Transdisciplinary Journal of Engineering and Science. 16, 177–207. DOI: https://doi.org/10.22545/2025/00276
[8] Leon, C., Lipuma, J., 2025. From disciplinary silos to cyber-transdisciplinary networks: A plural epistemic model for AGI-era knowledge production. Journal of Systemics, Cybernetics and Informatics. 23(7), 102–115. DOI: https://doi.org/10.54808/JSCI.23.07.102
[9] Zhang, W., Wang, S., Sun, J., et al., 2024. Global evolutionary trends of the discipline of engineering education and their empirical implications. Engineering Education Review. 2(4), 145–160. DOI: https://doi.org/10.54844/eer.2024.0820
[10] Lin, J., 2017. The construction of China’s new engineering disciplines for the future. Tsinghua Journal of Education. 38(2), 26–35.
[11] Asbjornsen, O.A., Hamann, R.J., 2002. Toward a unified systems engineering education. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 30(2), 175–182.
[12] Michael, K., Pitt, J., Sargent, J., et al., 2024. Automating higher education through artificial intelligence? IEEE Transactions on Technology and Society. 5(3), 264–271. DOI: https://doi.org/10.1109/TTS.2024.3450694
[13] Baltà‐Salvador, R., El‐Madafri, I., Brasó‐Vives, E., et al., 2025. Empowering engineering students through artificial intelligence (AI): Blended human–AI creative ideation processes with ChatGPT. Computer Applications in Engineering Education. 33(1), e22817. DOI: https://doi.org/10.1002/cae.22817
[14] Chen, B., Cheng, J., Wang, C., et al., 2025. Pedagogical biases in AI-powered educational tools: The case of lesson plan generators. The Social Innovations Journal. 30, 1–7. DOI: https://doi.org/10.31219/osf.io/zqjw5_v1
[15] Boon, M., Orozco, M., Sivakumar, K., 2022. Epistemological and educational issues in teaching practice-oriented scientific research: Roles for philosophers of science. European Journal for Philosophy of Science. 12(1), 16. DOI: https://doi.org/10.1007/s13194-022-00447-z
[16] Henriksen, D., Mishra, P., Woo, L., et al., 2025. The education doctorate in the context of generative artificial intelligence: Epistemic shifts and challenges to practical wisdom. Impacting Education: Journal on Transforming Professional Practice. 10(1), 73–79. DOI: https://doi.org/10.5195/ie.2025.485
[17] Hernández-de-Menéndez, M., Vallejo Guevara, A., Tudón Martínez, J.C., et al., 2019. Active learning in engineering education. A review of fundamentals, best practices and experiences. International Journal on Interactive Design and Manufacturing. 13(3), 909–922. DOI: https://doi.org/10.1007/s12008-019-00557-8
[18] Törngren, M., Grogan, P.T., 2018. How to deal with the complexity of future cyber-physical systems? Designs. 2(4), 40. DOI: https://doi.org/10.3390/designs2040040
[19] Horváth, I., 2025. Deriving manageable transdisciplinary research models for complicated problematics associated with next-generation cyber-physical systems: Part 3 - Constructing Research Models. Transdisciplinary Journal of Engineering & Science. 16. DOI: https://doi.org/10.22545/2025/00267
[20] Liu, Q., Tran, H., 2022. Exploring transdisciplinarity in engineering education and practice: A review of literature and existing initiatives. In Proceedings of the Canadian Engineering Education Association, Toronto, ON, Canada, 19–22 June 2022. DOI: https://doi.org/10.24908/pceea.vi.15953
[21] Lyngdorf, N.E., Jiang, D., Du, X., 2024. Frameworks and models for digital transformation in Engineering Education: A literature review using a systematic approach. Education Sciences. 14(5), 519. DOI: https://doi.org/10.3390/educsci14050519
[22] Keller, F., 2023. The concept of embodied human intelligence: Power and limits. Acta Philosophica: Rivista Internazionale di Filosofia. 32(1), 55–74.
[23] Nakajima, K., Hauser, H., Kang, R., et al., 2013. A soft body as a reservoir: Case studies in a dynamic model of octopus-inspired soft robotic arm. Frontiers in Computational Neuroscience. 7, 91. DOI: https://doi.org/10.3389/fncom.2013.00091
[24] Cangelosi, A., Bongard, J., Fischer, M.H., et al., 2015. Embodied intelligence. In Springer Handbook of Computational Intelligence. Springer: Berlin, Germany. pp. 697–714. DOI: https://doi.org/10.1007/978-3-662-43505-2_37
[25] Hauser, H., Ijspeert, A.J., Füchslin, R.M., et al., 2011. Towards a theoretical foundation for morphological computation with compliant bodies. Biological Cybernetics. 105, 355–370. DOI: https://doi.org/10.1007/s00422-012-0471-0
[26] Baldassarre, G., Granato, G., 2020. Goal-directed manipulation of internal representations is the core of general-domain intelligence. Journal of Artificial General Intelligence. 11(2), 19–23.
[27] Sitti, M., 2021. Physical intelligence as a new paradigm. Extreme Mechanics Letters. 46, 101340. DOI: https://doi.org/10.1016/j.eml.2021.101340
[28] Nakajima, K., 2020. Physical reservoir computing—An introductory perspective. Japanese Journal of Applied Physics. 59(6), 060501.
[29] Dodig-Crnkovic, G., 2013. The info-computational nature of morphological computing. In Philosophy and Theory of Artificial Intelligence. Springer Berlin Heidelberg: Berlin, Heidelberg. pp. 59–68. DOI: https://doi.org/10.1007/978-3-642-31674-6_5
[30] Zhang, P., Zhou, J., Chen, J., 2021. Form-finding of complex tensegrity structures using constrained optimization method. Composite Structures. 268, 113971. DOI: https://doi.org/10.1016/j.compstruct.2021.113971
[31] Cheng, B., Li, M., Lin, M., et al., 2025. Mechanobiology across timescales. Nature Reviews Physics. 7(11), 621–644. DOI: https://doi.org/10.1038/s42254-025-00874-w
[32] González-Martín, M., Martínez-Ara, G., Ngo, J.T., et al., 2025. Synthetic mechanotransduction. Nature Reviews Bioengineering. 4, 236–249. DOI: https://doi.org/10.1038/s44222-025-00366-7
[33] Sadiku, M.N., Wang, Y., Cui, S., et al., 2017. Cyber-physical systems: A literature review. European Scientific Journal. 13(36), 52–58. DOI: https://doi.org/10.19044/esj.2017.v13n36p52
[34] Möller, D.P.F., 2016. Guide to Computing Fundamentals in Cyber-Physical Systems. Springer: Heidelberg, Germany. pp. 307–375. DOI: https://doi.org/10.1007/978-3-319-25178-3
[35] Horváth, I., Gerritsen, B.H., 2012. Cyber-physical systems: Concepts, technologies and implementation principles. In Proceedings of the Tools and Methods of Competitive Engineering, Karlsruhe, Germany, 7–11 May 2012; pp. 7–21.
[36] Qadir, J., 2023. Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In Proceedings of the 2023 IEEE Global Engineering Education Conference, Salmiya, Kuwait, 1–4 May 2023; pp. 1–9. DOI: https://doi.org/10.36227/techrxiv.21789434.v1
[37] Banerjee, A., Venkatasubramanian, K.K., Mukherjee, T., et al., 2011. Ensuring safety, security, and sustainability of mission-critical cyber-physical systems. Proceedings of the IEEE. 100(1), 283–299. DOI: https://doi.org/10.1109/JPROC.2011.2165689
[38] Horváth, I., Rusák, Z., Li, Y., 2017. Order beyond chaos: Introducing the notion of generation to characterize the continuously evolving implementations of cyber-physical systems. In Proceedings of the ASME 2017 Computers and Information in Engineering Conference, Cleveland, OH, USA, 6–9 August 2017; pp. 1–14. DOI: https://doi.org/10.1115/DETC2017-67082
[39] Horváth, I., 2014. What the design theory of social-cyber-physical systems must describe, explain and predict? In An Anthology of Theories and Models of Design: Philosophy, Approaches and Empirical Explorations. Springer: London, UK. pp. 99–120.
[40] Sama, L.M., Welcomer, S.A., Gerde, V.W., 2003. Ecological citizenship: Principles, processes, and outcomes. Proceedings of the International Association for Business and Society. 14, 251–256. DOI: https://doi.org/10.5840/iabsproc20031432
[41] Colombo, A.W., Karnouskos, S., Bangemann, T., 2014. Towards the next generation of industrial cyber-physical systems. In Industrial Cloud-based Cyber-Physical Systems: The IMC-AESOP Approach. Springer International Publishing: Cham, Switzerland. pp. 1–22. DOI: https://doi.org/10.1007/978-3-319-05624-1_1
[42] Oks, S.J., Jalowski, M., Lechner, M., et al., 2024. Cyber-physical systems in the context of Industry 4.0: A review, categorization and outlook. Information Systems Frontiers. 26(5), 1731–1772. DOI: https://doi.org/10.1007/s10796-022-10252-x
[43] Jeganathan, L., Khan, A.N., Raju, J.K., et al., 2018. On a frame work of curriculum for engineering education 4.0. In Proceedings of the 2018 World Engineering Education Forum—Global Engineering Deans Council, Albuquerque, NM, USA, 12–16 November 2018; pp. 1–6. DOI: https://doi.org/10.1109/WEEF-GEDC.2018.8629629
[44] Ning, H., Liu, H., 2015. Cyber-physical-social-thinking space based science and technology framework for the Internet of Things. Science China Information Sciences. 58(3), 1–19. DOI: https://doi.org/10.1007/s11432-014-5209-2
[45] Özer, İ., Erden, Z., 2022. A novel approach to systematic development of social robot product families. International Journal of Social Robotics. 14(7), 1711–1729. DOI: https://doi.org/10.1007/s12369-022-00906-w
[46] Downey, G.L., Dumit, J., Williams, S., 1995. Cyborg anthropology. Cultural Anthropology. 10(2), 264–269. DOI: https://doi.org/10.1525/can.1995.10.2.02a00060
[47] Blouin, D., Al-Ali, R., Iacono, M., et al., 2021. An ontological foundation for multi-paradigm modelling for cyber-physical systems. In Multi-Paradigm Modelling Approaches for Cyber-Physical Systems. Academic Press: Cambridge, MA, USA. pp. 9–43. DOI: https://doi.org/10.1016/B978-0-12-819105-7.00007-6
PDF