Exploring Fraction Comprehension and Interest in Elementary Education Through AI-Powered Personalized Learning

Abstract

Artificial intelligence systems that adapt instruction to individual learners are increasingly deployed in K-12 classrooms, yet empirical evidence on their effects in authentic elementary settings remains limited, particularly for students with mathematics learning difficulties. This dissertation examines AI-powered personalized learning during primary school fraction instruction, a domain that is foundational to later mathematics and STEM achievement. The first manuscript presents a systematic review of research on artificial intelligence in mathematics education published between 2020 and 2024. The second manuscript reports a quasi-experimental study evaluating Mathbot, a chatbot-based personalized learning platform, against business-as-usual classroom instruction. Repeated measures ANOVA was used to assess change in fraction comprehension and situational interest across time points. Results indicated modest improvements in fraction comprehension for students using Mathbot relative to traditional instruction, while changes in situational interest were not statistically significant. Findings suggest that automated personalization did not displace the instructional role of the teacher and that teacher decision-making remained central to student outcomes. The work contributes classroom-based evidence to ongoing discussion about the capabilities and limits of adaptive AI systems in elementary mathematics, and about accessibility and equity considerations when such systems are used with students with disabilities.