Using AI for the Personalization of Mathematics and Science Education in Students

Source abstract

This research review explores the role of artificial intelligence (AI) in personalizing mathematics and science education to enhance student learning experiences and outcomes. The study synthesizes current research to examine how AI-driven technologies—such as adaptive learning systems, intelligent tutoring, and real-time feedback mechanisms—support individualized instruction aligned with students’ learning styles, paces, and cognitive needs. Findings indicate that AI significantly improves engagement, conceptual understanding, and problem-solving skills by leveraging data analytics and machine learning to deliver tailored content. These systems are grounded in established educational theories, including Mastery Learning and the Zone of Proximal Development. However, challenges remain, including unequal access to technology, algorithmic bias, data privacy concerns, and limited teacher preparedness, which hinder equitable implementation. The review also identifies gaps in longitudinal and context-specific research, particularly in under-resourced educational settings. The study concludes that while AI holds transformative potential for STEM education, its effective integration requires ethical design, inclusive policies, teacher training, and pedagogical alignment. For sustainable impact, AI should be implemented as a supportive tool within human-centered educational frameworks rather than a standalone solution.