Integration Of AI In Secondary Mathematics Education: A Review Of Trends And Pedagogical Implications

Abstract

Artificial Intelligence (AI) has emerged as a prominent technological intervention in secondary mathematics education, offering tools such as intelligent tutoring systems, adaptive learning environments, automated assessment, and learning analytics. While existing research frequently reports positive effects on student performance and instructional efficiency, a closer examination reveals that these gains are uneven, context-sensitive, and predominantly limited to procedural outcomes. This review critically synthesises empirical and conceptual studies on AI integration in secondary mathematics education, examining underlying pedagogical assumptions, methodological robustness, and classroom relevance. The analysis identifies persistent limitations related to narrow domain focus, algorithm-driven personalisation, reduced teacher agency, and insufficient support for higher-order mathematical reasoning. The review argues that, despite technological sophistication, current AI applications tend to optimise established instructional practices rather than fundamentally transform mathematical learning.