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Beyond Mastery: Toward a Broader Understanding of AI in Education

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Fig. 1

(source: Tuomi, 2022)

Notes

  1. Formal modeling of such qualitative changes requires the use of category theory, see (Ehresmann & Vanbremeersch, 2007).

  2. Historically, mainstream AI researchers have struggled with these epistemological questions since the 1960s when Hubert Dreyfus used Husserlian phenomenology to argue that strong symbolic AI was a dead end. Edward Feigenbaum’s reaction to Dreyfus’s critique is illustrative: “Phenomenology! That ball of fluff! That cotton candy!” (quoted in McCorduck, 1979, p. 197).

References

  • Anderson, P., & Tushman, M. L. (1990). Technological discontinuities and dominant designs. Administrative Science Quarterly, 35, 604–633.

  • Baker, R. S., Boser, U., & Snow, E. L. (2022). Learning engineering: A view on where the field is at, where it’s going, and the research needed. Technology, Mind, and Behavior, 3(1),1–23. https://doi.org/10.1037/tmb0000058.

  • Bergson, H. (1983). Creative Evolution (first edition 1907). University Press of America.

  • Biesta, G. (2010). Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge.

  • Biesta, G. (2015). How does a competent teacher become a good teacher? On judgement, wisdom and virtuosity in teaching and teacher education. In R. Heilbronn, & L. Foreman-Peck (Eds.), Philosophical Perspectives on the Future of Teacher Education (pp.3–22). Wiley Blackwell.

  • Bloom, B. S. (1978). New views of the learner: Implications for instruction and curriculum. Educational Leadership, 35(7), 563–576.

  • Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16. https://doi.org/10.3102/0013189X013006004.

  • Brown, J. S., & Duguid, P. (1991). Organizational learning and communities of practice: Toward a unified view of working, learning, and innovation. Organization Science, 2(1), 40–57.

  • Bull, S. (2020). There are Open Learner Models about! IEEE Transactions on Learning Technologies, 13(2), 425–448. https://doi.org/10.1109/TLT.2020.2978473.

  • Chernyshenko, O. S., Kankaraš, M., & Drasgow, F. (2018). Social and emotional skills for student success and well-being: Conceptual framework for the OECD study on social and emotional skills. OECD. https://doi.org/10.1787/db1d8e59-en.

  • Cole, M. (1974). In M. Cole and S. Scribner (Eds.), Culture and Thought: A Psychological Introduction. John Wiley & Sons.

  • Dewey, J. (1958). Experience and Nature. Dover Publications, Inc.

  • Drucker, P. (1993). Post-Capitalist Society. HarperBusiness.

  • du Boulay, B. (2019). Escape from the Skinner Box: The case for contemporary intelligent learning environments. British Journal of Educational Technology, 50(6), 2902–2919. https://doi.org/10.1111/bjet.12860.

  • Ehresmann, A., & Vanbremeersch, J. P. (2007). Memory Evolutive Systems. Elsevier.

  • Engeström, Y. (1987). Learning by Expanding: An Activity Theoretical Approach to Developmental Work Research. Orienta Konsultit.

  • Guskey, T. R. (2007). Closing achievement gaps: Revisiting Benjamin S. Bloom’s “Learning for Mastery”. Journal of Advanced Academics, 19(1), 8–31. https://doi.org/10.4219/jaa-2007-704.

  • Guskey, T. R. (2012). Mastery learning. In N.M.Seel (Ed.), Encyclopedia of the Sciences of Learning (pp.2097–2100). Springer US. https://doi.org/10.1007/978-1-4419-1428-6_1553.

  • Harré, R., Clarke, D., & Carlo, N. D. (1985). Motives and Mechanisms: An Introduction to the Psychology of Action. Methuen & Co. Ltd.

  • Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570.

  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Buckingham Shum, S., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-021-00239-1.

  • Illeris, K. (2018). An overview of the history of learning theory. European Journal of Education, 53(1), 86–101. https://doi.org/10.1111/ejed.12265.

  • Jarvis, P. (2007). Globalization, Lifelong Learning and the Learning Society: Sociological Perspectives. Routledge.

  • Kay, J., Bartimote, K., Kitto, K., Kummerfeld, B., Liu, D., & Reimann, P. (2022). Enhancing learning by Open Learner Model (OLM) driven data design. Computers and Education: Artificial Intelligence, 3, 100069. https://doi.org/10.1016/j.caeai.2022.100069.

  • Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge University Press.

  • Leont’ev, A. N. (1978). Activity, Consciousness, and Personality. Prentice-Hall.

  • Luria, A. R., & Vygotsky, L. (1992). Ape, Primitive Man, and Child: Essays in the History of Behavior. Harvester Wheatsheaf.

  • McCorduck, P. (1979). Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. W.H. Freeman and Company.

  • Miettinen, R. (2000). The concept of experiential learning and John Dewey’s theory of reflective thought and action. International Journal of Lifelong Education, 9(1), 54–72.

  • Nonaka, I. (1991). The knowledge-creating company. Harvard Business Review, November-December, 96–104.

  • Nonaka, I., Toyama, R., & Hirata, T. (2008). Managing Flow: A Process Theory of the Knowledge-Based Firm. Palgrave Macmillan.

  • Polanyi, M. (1967). The Tacit Dimension. Anchor.

  • Schön, D. A. (1983). The Reflective Practitioner. Basic Books.

  • Sen, A. (1993). Capability and well-being. In M. C. Nussbaum, & A. Sen (Eds.), The Quality of Life (pp.30–53). Clarendon Press.

  • Skinner, B. F. (1965). Review lecture:The technology of teaching. Proceedings of the Royal Society of London, 162(989), 427–443.

  • Suchman, L. (1987). Plans and Situated Actions: The Problem of Human-Machine Communication. Cambridge University Press.

  • Tuomi, I. (2000). Data is more than knowledge: Implications of the reversed knowledge hierarchy to knowledge management and organizational memory. Journal of Management Information Systems, 6(3), 103–117. https://doi.org/10.1080/07421222.1999.11518258.

  • Tuomi, I. (2002). Networks of Innovation: Change and Meaning in the Age of the Internet. Oxford University Press.

  • Tuomi, I. (2012). Foresight in an unpredictable world. Technology Analysis & Strategic Management, 24(8), 735–751. https://doi.org/10.1080/09537325.2012.715476.

  • Tuomi, I. (2017). Ontological expansion. In R. Poli (Ed.), Handbook of Anticipation (pp.37–71). Springer International Publishing. https://doi.org/10.1007/978-3-319-31737-3_4-1.

  • Tuomi, I. (2018a). The Impact of Artificial Intelligence on Learning, Teaching, and Education: Policies for the Future. Publications Office of the European Union. https://doi.org/10.2760/12297.

  • Tuomi, I. (2018b). Vygotsky meets backpropagation: Artificial neural models and the development of higher forms of thought. In C.P. Rosé, R. Martínez-Maldonado, U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. duBoulay (Eds.), Artificial Intelligence in Education. AIED 2018 (Vol.10947). Springer. https://doi.org/10.1007/978-3-319-93843-1_42.

  • Tuomi, I. (2022). Artificial intelligence, 21st century competences, and socio-emotional learning in education: More than high-risk? European Journal of Education, 57(4), 601–619. https://doi.org/10.1111/ejed.12531.

  • UNESCO (1996). Learning: The Treasure Within. UNESCO.

  • Vygotsky, L. (1986). Thought and Language. The MIT Press.

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    Ilkka Tuomi

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Tuomi, I. Beyond Mastery: Toward a Broader Understanding of AI in Education. Int J Artif Intell Educ 34, 20–30 (2024). https://doi.org/10.1007/s40593-023-00343-4

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