Impact of AI-Driven Personalized Learning in Educational Systems on Policy Development - Olaoye Olumuyiwa Joseph; Ajilore Opeoluwa Omotayo (Britain International of Linguistics Arts and Education (BIoLAE) Journal, 2026)
The integration of AI-driven personalized learning within educational systems is transforming the landscape of policy development. This abstract explores the profound implications of these technologies on various educational policies, including curriculum design, assessment practices, and teacher training initiatives. Personalized learning models, powered by artificial intelligence, offering tailored educational experiences that cater to individual student needs, thereby enhancing engagement and academic achievement. As educators leverage data analytics and adaptive learning technologies, policymakers are prompted to rethink traditional frameworks to create adaptable, inclusive, and equitable educational environments. The findings will underscore the necessity for policies that facilitate the integration of AI tools while ensuring responsible use of data, equity in access, and support for teachers. This discourse will also highlight the importance of ongoing professional development for educators to navigate these innovations effectively. Ultimately, AI-driven personalized learning not only reshapes instructional methodologies but also necessitates a re-evaluation of policy structures to foster an ecosystem conducive to continuous improvement and innovation in education.