Adaptive learning, supported by artificial intelligence (AI), represents a major innovation in education, with a significant impact on inclusivity. This approach allows the personalisation of the learning experience by adapting in real time to the individual needs of students, responding to specific difficulties and optimizing interaction with educational content. The paper analyzes the main educational pathways according to the individual needs of students, with a particular focus on students with special educational needs (SEN) or students with disadvantaged socioeconomic backgrounds. The opportunities offered by AI in reducing inequalities, improving equitable access to education, will be discussed. The role of teachers in educational co-design is central to this process, as their experience and understanding of students' needs is crucial for optimizing the use of adaptive technologies. The contribution will also address critical challenges related to the integration of AI into education systems, such as equity in access to technologies, teacher training for the effective use of adaptive tools, and ethical issues related to the privacy of student data.
References
Ainscow M., Booth T. (2006). Improving Schools, Developing Inclusion. Routledge.
Ainscow M., Miles S. (2008). Making Education for All Inclusive: Where Next?. Prospects, 38(1): 15-34.
Ainscow M., Miles S., and Dyson A. (2008). Making Sense of Inclusive Education: Key Issues and Debates. London: Routledge.
Baker R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2): 600-614.
Binns R. (2018). Ethical issues in the use of big data in education. In M. J. K. Leung (Ed.), Big Data in Education: A Critical Overview (pp. 183-202). Springer.
Booth T., Ainscow M. (2011). The Index for Inclusion: Developing Learning and Participation in Schools. Centre for Studies on Inclusive Education.
Braman S. (2009). The Information Revolution and the Digital Divide: Implications for Policy and Society. Lawrence Erlbaum Associates.
Conati C., Kardan S. (2013). AI in Education: Adaptive Learning Systems. In J. C. Lester, R. M. Vicari, and F. P. Santos (Eds.), Proceedings of the International Conference on Artificial Intelligence in Education (pp. 389-398).
Ertmer P. A. (1999). Addressing First- and Second-Order Barriers to Change: Strategies for Technology Integration. Educational Technology Research and Development, 47(4): 47-61.
Florian L., Black-Hawkins K. (2011). Exploring Inclusive Pedagogy. Cambridge Journal of Education, 41(2): 173-190.
Freire P. (1970). Pedagogy of the Oppressed. New York: Herder and Herder.
Gurung R. A. R., Schwartz M. (2020). Ethics of AI in Education: Designing for equity and inclusion. Journal of Educational Technology Development and Exchange, 13(1): 1-12.
Guskey T. R. (2002). Professional Development and Teacher Change. Teachers and Teaching: Theory and Practice, 8(3): 381-391.
Holmes W. et al. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. OECD Publishing.
Hattie J., Timperley H. (2007). The Power of Feedback. Review of Educational Research, 77(1): 81-112.
Kolb D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall.
Kessler E., Massey M. (2020). Adaptive Learning and Inclusive Practices. Education-al Technology Research and Development, 68(3): 529-545.
Luckin R., Holmes W., Griffiths M., and Forcier L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.
OECD (2016). Skills for the Digital Age: The Role of Education and Training. OECD Publishing.
OECD (2020). The Future of Education and Skills 2030: OECD Education Working Paper No. 48. OECD Publishing.
Pane J. F. et al. (2017). Effectiveness of Cognitive Tutor Algebra I at Scale. Journal of Research on Educational Effectiveness, 10(3): 408-433.
Ryan R. M., Deci E. L. (2000). Self-Determination Theory and the Facilitation of In-trinsic Motivation. American Psychologist, 55(1): 68-78.
Selwyn N. (2016). Is Technology Good for Education?. Polity Press.
Slee R. (2011). The Irregular School: Exclusion, Schooling and Inclusive Education. Routledge.
Siemens G. (2004). Connectivism: A Learning Theory for the Digital Age. International Journal of Instructional Technology & Distance Learning, 2(1): 3-10.
Tomlinson C. A. (2001). How to Differentiate Instruction in Mixed-Ability Class-rooms. ASCD.
UNESCO (2020). Inclusion and Education: All Means All. Paris: UNESCO Publishing.
Van Dijk J. (2020). The Digital Divide: How the Internet is Creating a New Class System. SAGE Publications.
Zhou X., Brown D. (2020). The Ethics of AI in Education: Protecting Students’ Privacy and Preventing Bias. Journal of Educational Ethics, 22(1): 23-34.