Effects of Personalization in Large Language Model Tutors on Cognitive Load during Mathematics Learning

The use of Large Language Models (LLMs) in education has expanded rapidly, with LLM tutors increasingly proposed to support learning through individualized explanations and interactions. However, empirical evidence for their effectiveness has remained mixed, particularly for demanding domains such as mathematics, and the conditions under which personalization is beneficial remain poorly understood. Additionally, effects on learning may be captured by changes in cognitive and behavioral processes than by immediate learning performance alone. Accordingly, this study examined whether personalization in LLM tutors influenced learning-related cognitive processes during mathematics learning. A multimodal approach was used with perceptual, behavioral, and physiological measures, using pupillometry. A custom LLM tutoring interface was developed to enable control over system-level prompts, minimize extraneous stimuli, standardize instructions and capture interaction data. The tutor’s communicative style, tone, and explanatory structure were adapted via system-level prompts to one of two Felder–Silverman–derived categories, based on pretask questionnaire responses. 40 participants completed three learning blocks, each with a mathematics topic, under personalized or non-personalized conditions. Blocks were each followed by short quizzes. Results showed no significant differences in learning accuracy. However, personalized tutoring showed significantly lower cognitive load, reflected in decreased pupil dilation, alongside trends in 3 behavioral measures consistent with more active engagement. These findings suggest that personalization alters cognitive resource allocation during complex learning tasks, highlighting the need to evaluate AI-supported learning beyond immediate test performance. Future studies should examine whether such cognitive and engagement changes translate into learning gains over time.