One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction

This paper presents PACE, a personalized conversational AI tutoring agent for K-12 mathematics instruction that adapts teaching strategies based on students' learning styles (using the Felder-Silverman model) and employs Socratic teaching methods to provide individualized feedback and scaffolding.

Large language models (LLMs) have been increasingly employed in various intelligent educational systems, simulating human tutors to facilitate effective human-machine interaction. However, previous studies often overlook the significance of recognizing and adapting to individual learner characteristics. Such adaptation is crucial for enhancing student engagement and learning efficiency, particularly in mathematics instruction, where diverse learning styles require personalized strategies to prom

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