Personalized learning in mathematics education through ai: bridging theoretical foundations with practical classroom applications and adaptive instructional strategies
DOI:
https://doi.org/10.63688/m6ceew69Keywords:
Personalized learning, Artificial intelligence, Mathematics education, Intelligent tutoring systems, Automated assessmentAbstract
The integration of artificial intelligence (AI) in education is significantly reshaping teaching practices, time management for educators, and teacher-student interactions. This article presents an updated review of research published between 2020 and 2025, focusing on the most effective strategies for implementing AI in school environments. The analysis highlights innovative tools such as virtual assistants, educational chatbots, academic data analysis platforms, and automated assessment systems, all of which have contributed to a more personalized approach to teaching by adapting instruction to individual student needs. AI also helps reduce teachers’ administrative burden by automating repetitive tasks, allowing them to focus more on meaningful and reflective teaching. However, notable challenges remain, including limited teacher training in AI technologies, resistance to innovation in traditional educational settings, and ethical concerns regarding the handling of student data. The findings emphasize the importance of providing advanced digital training for educators and fostering an institutional culture that embraces technological transformation. Further research is recommended to explore the use of AI in diverse educational contexts, along with the development of regulatory frameworks to ensure ethical, efficient, and sustainable implementation. This analysis offers a substantial contribution to understanding how AI can enhance educational quality, optimize instructional time, and enrich pedagogical practices through practical and adaptive strategies.
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