Agentic AI in Inclusive Learning: A Framework for Autonomous Personalization across Diverse Learner Populations
DOI:
https://doi.org/10.63282/3050-922X.AECTIC-114Keywords:
Agentic AI, Inclusive Education, Adaptive Learning, Universal Design for Learning, Intelligent Tutoring Systems, Educational Equity, Algorithmic FairnessAbstract
The emergence of agentic artificial intelligence represents a fundamental shift in how educational technology can address the needs of diverse learner populations. Unlike traditional adaptive learning systems that respond to predefined triggers, agentic AI operates with genuine autonomy, pursuing complex learning objectives while continuously adapting strategies based on evolving learner states. This paper presents a comprehensive framework for deploying agentic AI in inclusive educational environments, organized around ten interconnected pillars: personalized learning pathways, dynamic scaffolding, multimodal experiences, accessibility enhancements, continuous learner modelling, autonomous feedback, cultural and linguistic inclusivity, teacher augmentation, motivational support, and ethical guardrails. Drawing on Self Determination Theory and Universal Design for learning principles, we argue that agentic AI systems must satisfy fundamental psychological needs for autonomy, competence, and relatedness while providing multiple means of engagement, representation, and expression. The framework addresses critical implementation challenges including algorithmic bias, data privacy, and the preservation of human agency in educational decision making. We propose architectural specifications for responsible deployment and discuss implications for educational equity. This work contributes both theoretical grounding and practical guidance for researchers and practitioners seeking to harness agentic AI capabilities while maintaining commitment to inclusive, learner centred education.
References
[1] Acharya, A., Smith, J., Chen, L., & Williams, R. (2025). Agentic AI systems: Architecture and applications. Journal of Artificial Intelligence Research, 74, 18901-18945.
[2] Anderson, J. R., Boyle, C. F., & Reiser, B. J. (1985). Intelligent tutoring systems. Science, 228(4698), 456-462.
[3] Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32(4), 1052-1092. https://doi.org/10.1007/s40593-021-00285-9
[4] Batsaikhan, O., & Correia, A. P. (2024). The effects of generative artificial intelligence on intelligent tutoring systems in higher education. STEM Education and Learning, 4(1).
[5] 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.
[6] Carbonell, J. R. (1970). AI in CAI: An artificial intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems, 11(4), 190-202.
[7] CAST. (2024). Universal design for learning guidelines version 3.0. https://udlguidelines.cast.org/
[8] Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modelling the acquisition of procedural knowledge. User Modelling and User-Adapted Interaction, 4(4), 253-278.
[9] Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behaviour. Plenum Press.
[10] Deci, E. L., & Ryan, R. M. (2000). The 'what' and 'why' of goal pursuits: Human needs and the self-determination of behaviour. Psychological Inquiry, 11(4), 227-268.
[11] Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112.
[12] Hosseini, M., & Seilani, H. (2025). Agentic AI in educational contexts: From copilot to autopilot. Educational Technology Research and Development, 73(2), 445-472.
[13] Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems in K-12 education. npj Science of Learning, 10, 29. https://doi.org/10.1038/s41539-025-00320-7
[14] Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901-918.
[15] Neshaei, S. P., Davis, R. L., Hazimeh, A., Lazarevski, B., Dillenbourg, P., & Käser, T. (2024). Towards modelling learner performance with large language models. Proceedings of the Educational Data Mining Conference 2024.
[16] Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education, 7(2), 133-144.
[17] Pérez Perez, F. (2024). AI and accessibility in education. Consortium for School Networking & CAST.
[18] Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in Neural Information Processing Systems, 28, 505-513.
[19] Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. ASCD.
[20] Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78.
[21] Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.
[22] Saborío-Taylor, S., & Rojas-Ramírez, F. (2024). Universal design for learning and artificial intelligence in the digital era: Fostering inclusion and autonomous learning. International Journal of Professional Development, Learners and Learning, 6(2), ep2408.
[23] Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
[24] Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89-100.
[25] World Health Organization. (2011). World report on disability. WHO Press.
