Evaluating AI-Driven Adaptive Learning Models in Mathematics: A Contemporary Perspective

Year : 2026 | Volume : 03 | Issue : 01 | Page : 8 12
    By

    Munish Kumar,

  1. Assistant Professor, Department of Mathematics, DAV College, Bathinda, Punjab, India

Abstract

Artificial Intelligence (AI) continues to transform mathematics education through data-driven personalization and adaptive learning technologies. This study investigates how AI-enabled adaptive platforms influence student performance and engagement in mathematics classrooms. Using a quantitative approach across two institutions, pre- and post-assessment results were compared between students using AI-assisted adaptive learning tools and those receiving conventional instruction. The findings reveal that AI-driven learners demonstrated significantly higher gains in conceptual understanding and engagement metrics, indicating the potential of adaptive algorithms to tailor learning paths effectively. While these outcomes highlight the pedagogical value of intelligent systems, the research also identifies persistent challenges, including algorithmic bias, privacy risks, and uneven access. The study concludes that the responsible integration of AI in mathematics education can create a more inclusive, participatory, and outcome-oriented learning environment.

Keywords: Adaptive learning, AI-powered education, artificial intelligence (AI), data-driven learning, EdTech, machine learning in education, pedagogical innovation, personalized learning

[This article belongs to Recent Trends in Mathematics ]

How to cite this article:
Munish Kumar. Evaluating AI-Driven Adaptive Learning Models in Mathematics: A Contemporary Perspective. Recent Trends in Mathematics. 2026; 03(01):8-12.
How to cite this URL:
Munish Kumar. Evaluating AI-Driven Adaptive Learning Models in Mathematics: A Contemporary Perspective. Recent Trends in Mathematics. 2026; 03(01):8-12. Available from: https://journals.stmjournals.com/rtm/article=2026/view=239215


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Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 27/12/2025
Accepted 21/01/2026
Published 31/01/2026
Publication Time 35 Days
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