PROMOTING INFERENTIAL PROCESSES IN EDUCATIONAL CONTEXTS IN THE AGE OF ARTIFICIAL INTELLIGENCE

Antonella Nuzzaci

Abstract


In the age of artificial intelligence, fostering inferential processes presents a crucial educational challenge. This paper explores how different forms of inference—deduction, induction, and abduction—are embedded in both human reasoning and computational models and examines how they can be effectively promoted in school and university settings. By comparing natural cognition with AI system architectures (symbolic, sub-symbolic, and neuro-symbolic), the article proposes instructional strategies, digital tools, and learning environments designed to support critical thinking, intuitive reasoning, and epistemic responsibility. Special attention is given to Explainable AI (XAI) as a pedagogical lever, to the cultivation of metacognitive skills, and to the role of artificial intelligence as an epistemic partner. The aim is to outline an educational model that integrates logical rigor, inferential creativity, and responsible digital citizenship.


Full Text:

PDF

References


Ane, T. A., & Nepa, T. (2024). Emerging artificial intelligence prediction model for educational knowledge representation through learning performance. Research on Education and Media, 16(2). https://doi.org/10.2478/rem-2024-0011.

Bilotti, U., Campitiello, L., Todino, M. D., & Sibilio, M. (2023). Emulation and understanding the emotion according to generative artificial intelligence - case study of emotional component extracted from visual artworks. Journal of Inclusive Methodology and Technology in Learning and Teaching, 3(4), 1-9

Bruner, J. S. (2016). Il processo dell'educazione: Dopo Dewey (1960). Roma: Armando.

Chen, J., Liao, Q. V., Vaughan, J. W., & Bansal, G. (2023). Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations. arXiv:2301.11692. https://doi.org/10.48550/arXiv.2301.07255

Cheraghi, R., Mahfoozi, A. M., Zolfaghari, S., Shabani, M., Ramezani, M., & Rabiee, H. R. (2025). Epistemic uncertainty aware recommendation systems via Bayesian deep ensemble learning [arXiv preprint arXiv:2504.10753]. https://doi.org/10.48550/arXiv.2504.10753.

Davis, R., & Lenat, D. B. (1982). Knowledge-Based Systems in Artificial Intelligence. New York: McGraw-Hill.

Di Tore, P. A. (2023). Intelligenza Artificiale e processi educativi secondo l’Intelligenza Artificiale. QTimes – webmagazine, 2(1), 464-481.

Eco, U. (1979). Lector in fabula: La cooperazione interpretativa nei testi narrativi. Milano: Bompiani.

Feigenbaum, E. A., & Earnest, L. D. (1977). The art of artificial intelligence: I. Themes and case studies of knowledge engineering. Stanford: Stanford University, Department of Computer Science, Technical Reports.

Floridi, L. (2023). The Ethics of Artificial Intelligence in Education. Oxford: Oxford University Press.

Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI-Explainable artificial intelligence. Science robotics, 4(37), eaay7120. doi:10.1126/scirobotics.aay7120. PMID: 33137719.

Hassani, H. & Silva, E.S. (2024). Predictions from Generative Artificial Intelligence Models: Towards a New Benchmark in Forecasting Practice. Information, 15(6), 291.

Hassani, H., & Silva, E. S. (2023). The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field. Big Data and Cognitive Computing (BDCC), 7(2), 62, 1-16. 10.3390/bdcc7020062.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: The Center for Curriculum Redesign.

Ignatiev, A., Narodytska, N., & Marques-Silva, J. (2019). Abduction-Based Explanations for Machine Learning Models. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 1511-1519. https://doi.org/10.1609/aaai.v33i01.33011511.

Johnson-Laird, P. N., & Byrne, R. M. J. (1991). Deduction. Mahwah: Lawrence Erlbaum Associates.

Kahneman, D. (2011). Thinking, fast and slow. London: Penguin Books.

Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. London: UCL IOE Press.

McCarthy J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. Stanford: Computer Science Department Stanford, University Stanford.

Miller, T., & Davis, G. (2023). Explainable AI In Education: Current Trends, Challenges, And Opportunities. SoutheastCon 2023, Orlando, FL, USA, 2023, pp. 232-239, doi: 10.1109/SoutheastCon51012.2023.10115140.

Pedwell, C. (2023). Intuition as a “trained thing”: Sensing, thinking, and speculating in computational cultures. Subjectivity, 30, 348–372. https://doi.org/10.1057/s41286-023-00170-x.

Peirce, C. S. (1931–1958). Collected Papers of Charles Sanders Peirce (Vols. 1–8), ed. C. Hartshorne, P. Weiss & A. Burks. Cambridge, MA: Harvard University Press.

Polanyi, M. (1966). The tacit dimension. London: Routledge & Kegan Paul.

Redecker, C., & Punie, Y. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. Luxembourg: Publications Office of the European Union.

Suresh, H., & Guttag, J. (2021). A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. EAAMO 2021: Equity and Access in Algorithms, Mechanisms, and Optimization, arXiv:1901.10002v5. https://doi.org/10.48550/arXiv.1901.10002

Triberti, S., Di Fuccio, R., Scuotto, C., Marsico, E., & Limone, P. (2024). “Better than my professor?”How to develop artificial intelligence tools for higher education. Frontiers in ArtificialIntelligence, 7. https://doi.org/10.3389/FRAI.2024.1329605/FULL.

UNESCO. (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development. Paris: UNESCO.

Valiant, L. G. (2020). Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World (2013). New York: Basic Books.

Zanetti, M., Rendina, S., Piceci, L., & Peluso Cassese, F. (2020). Potential risks of Artificial Intelligence in education. Form@re - Open Journal Per La Formazione in Rete, 20(1), 368–378. https://doi.org/10.13128/form-8113.

Zhang, J. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1871-1885. https://doi.org/10.1111/jcal.12988.

Zhang, X., Guo, Y., Stepputtis, S., Sycara, K., & Campbell, J. (2023). Explaining Agent Behavior with Large Language Models. arXiv, 2309.10346. https://arxiv.org/abs/2309.10346.




DOI: https://doi.org/10.32043/gsd.v9i2_Sup.1557

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 ITALIAN JOURNAL OF HEALTH EDUCATION, SPORT AND INCLUSIVE DIDACTICS

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Italian Journal of Health Education, Sports and Inclusive Didactics 
ISSN printed: 2532-3296