The Digital Inclusion Ecosystem: AI, Adaptive Platforms, and New Pedagogical Challenges for the Italian School System
Parole chiave:
BES; DSA; Inclusione; Adaptive Learning; Scuola primariaAbstract
This paper analyzes the transformative potential of Artificial Intelligence (AI) and Open Educational Resources (OER) in promoting school inclusion, with particular reference to the Italian context. Through dialogue with leading technology acceptance models and teacher competence frameworks (DigCompEdu), it examines how the integration of Adaptive Learning and Learning Analytics systems can address Special Educational Needs (SEN) according to the Universal Design for Learning (UDL) perspective. A quasi-experimental study conducted on a sample of 62 primary school students is presented, aimed at evaluating the impact of an adaptive platform on learning and self-efficacy. The results show a significant improvement in the experimental group (d = 0.91), with particularly significant effects for students with SEN/DSA (d = 1.12), and a significant increase in perceived self-efficacy (d = 1.05).
Riferimenti bibliografici
Ainscow, M. (2020). Promoting inclusion and equity in education. Nordic Journal of Studies in Educational Policy, 6(1), 7–16. https://doi.org/10.1080/20020317.2020.1729587
Ainscow, M., & Messiou, K. (2018). Engaging with the views of students to foster inclusion in education. Journal of Educational Change, 19(1), 1–17.
Baker, R. S., & Siemens, G. (2022). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (3rd ed., pp. 471–492). Cambridge University Press.
Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.
Canevaro, A. (2013). Scuola inclusiva e mondo del lavoro. Erickson.
Chen, L., Chen, P., & Lin, Z. (2024). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21–29.
Cottini, L. (2017). Didattica speciale e inclusione scolastica. Carocci.
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.
Fiorucci, M., & Bevilacqua, A. (2025). Pedagogia, IA e giustizia sociale: Nuove frontiere per l’inclusione. FrancoAngeli.
Florian, L. (2014). What counts as evidence of inclusive education? European Journal of Special Needs Education, 29(3), 286–294.
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2023). Artificial intelligence and education: A critical view through the lens of human rights, democracy and the rule of law. Council of Europe.
Ianes, D. (2005). Bisogni Educativi Speciali e inclusione. Erickson.
Khosravi, H., Cooper, K. M., Kitto, K., Buckingham Shum, S., & Gašević, D. (2024). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 5, 100135.
Limone, P., & Toto, G. A. (2022). Apprendimento e nuove tecnologie. Progedit.
Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Press.
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2024). Intelligent tutoring systems and learning outcomes: A meta-analysis. Review of Educational Research, 94(2), 250–289.
OECD (2021). AI in education: Challenges and opportunities for sustainable development. OECD Publishing.
Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2017). Informing progress: Insights on personalized learning implementation and effects. RAND Corporation.
Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union.
Rivoltella, P. C. (2021). L’agire didattico. Manuale per l’insegnante. La Scuola SEI.
Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. ASCD.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.
UNESCO (2023). Global Education Monitoring Report 2023: Technology in education – A tool on whose terms? UNESCO.
Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning. Review of Educational Research, 81(1), 4–28.
Vassilaki, E., Pliakis, C., & Karagiannidis, C. (2023). Adaptive technologies for dyslexia: The iRead Project. British Journal of Educational Technology, 54(2), 412–430.
Vivanet, G. (2023). Learning Analytics e inclusione: Prospettive metodologiche. Rivista Italiana di Pedagogia Speciale, 19(1), 45–62.
Vygotskij, L. S. (1974). Apprendimento e sviluppo intellettuale nell’età scolastica. In A. Leont’ev, A. Lurija & A. Vygotskij, Psicologia e pedagogia (pp. 23–53). Editori Riuniti.
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–27.
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
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