Artificial intelligence in university mathematics education - Full Length Research Paper - Personalized tutoring support and learning analytics as factors of academic achievement, independent thinking and risk reduction

Oct 15, 2026

The paper examines how an AI-oriented approach to university mathematics instruction—combining personalized tutoring support with learning analytics—affects students’ academic achievement, independent thinking, and learning risk. Using a quasi-experimental mixed-methods design, it evaluates undergraduate participants via pre- and post-tests, system log data on interactions, and measures of engagement and independent problem-solving. Results link personalized AI tutoring to significant performance gains, stronger problem-solving strategies, and fewer academically at-risk students. Learning analytics further enables early detection of learning difficulties through predictive behavioral indicators.

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Abstract

Introduction

Artificial intelligence as a structural driver of transformation in higher mathematics education

Personalized AI tutoring in mathematics: Didactic foundations and empirical tensions

African EducaƟonal Research Journal Vol. 14(1), pp. 228-239, March 2026 ISSN: 2354-2160 Full Length Research Paper Artificial intelligence in university mathematics education: Personalized tutoring support and learning analytics as factors of academic achievement, independent thinking and risk reduction Roman Yavich Department of Mathematics, Ariel University, Israel. Accepted 24 February, 2026 | ABSTRACT
In recent years, artificial intelligence has been increasingly integrated into higher education, including the teaching of mathematics. However, its impact on students’ academic achievement and the development of independent thinking remains a subject of ongoing scholarly debate. Contemporary studies point to the potential of AI-supported learning environments while simultaneously emphasizing the risks of diminished cognitive autonomy when intelligent systems are used without pedagogical regulation. The present study aims to examine the effectiveness of an AI-oriented approach to mathematics instruction based on the combination of personalized tutoring support and learning analytics tools within a university educational context. Within a quasi-experimental mixed-methods design, an AI-supported instructional model was implemented, incorporating adaptive learning pathways, an intelligent tutor providing step-by-step scaffolding, and a system for analysing students’ learning behaviours. Participants were undergraduate students enrolled in university-level mathematics courses. The effectiveness of the approach was assessed using pre-and post-test results, log data capturing students’ interactions with the AI system, and indicators of academic engagement and independent problem-solving. The results demonstrate that the use of personalized AI-based tutoring is statistically significantly associated with improved academic performance, the development of independent problem-solving strategies, and a reduction in the proportion of students classified as academically at risk. Furthermore, learning analytics data analysis enabled the identification of behavioural indicators with predictive value for the early detection of learning difficulties. Overall, the findings confirm that, when integrated within a sound pedagogical framework, artificial intelligence does not replace students’ cognitive activity but rather functions as a supportive tool for fostering independent mathematical thinking and for informing evidence-based instructional decision-making. The practical significance of this study lies in the proposal of a reproducible AI-oriented model for mathematics education that can be applied across university courses of varying levels of complexity.
Keywords: Artificial intelligence in education, university mathematics education, personalized learning, intelligent tutoring systems, learning analytics, independent mathematical thinking, academic achievement, at-risk students. | | --- | | *Corresponding author. E-mail: [romany@g.ariel.ac.il](romany@g.ariel.ac.il). | INTRODUCTION Artificial intelligence as a structural driver of transformation in higher mathematics education Over the past decades, the digitalization of higher education has undergone a qualitative shift associated with the transition from traditional information technologies to intelligent systems based on artificial intelligence methods. In contemporary scholarly literature, AI is conceptualized not merely as a technological tool but as a transformative factor capable of reshaping pedagogical models, redefining instructor–student interactions, and altering the organisation of the instructional process itself Yavich 229 (Holmes et al. , 2022; Chiu et al. , 2023) . These transformations are particularly consequential in university-level mathematics education. Advanced mathematics courses are traditionally characterised by high levels of abstraction, substantial cognitive load, and strict requirements regarding students’ prior knowledge. A la

FAQs

What AI components does the study implement for university mathematics instruction?

The instructional model combines adaptive learning pathways, an intelligent tutor that provides step-by-step scaffolding, and a system that analyzes students’ learning behaviors through learning analytics.

How are learning outcomes and risks for students assessed?

Effectiveness is assessed using pre- and post-test results, log data capturing student interactions, and indicators covering academic engagement and independent problem-solving. The study also tracks the proportion of students classified as academically at risk.

What do the findings suggest about the role of AI in students’ cognitive development?

When integrated within a sound pedagogical framework, AI does not replace students’ cognitive activity; it acts as a supportive tool that helps foster independent mathematical thinking and supports evidence-based instructional decisions.

Oct 15, 2026

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