A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression

Interleaved practice enhances the memory and problem-solving ability of students in undergraduate courses. We introduce a personalized learning tool built on a Large Language Model (LLM) that can provide immediate and personalized attention to students as they complete homework containing problems i...

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Bibliographic Details
Published in2024 IEEE Conference on Artificial Intelligence (CAI) pp. 38 - 43
Main Authors Zhu, Yufan, Khoo, Zi-Yu, Choong Low, Jonathan Sze, Bressan, Stephane
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.06.2024
Subjects
AI and Education
Knowledge engineering
Large language models
Learning (artificial intelligence)
Mathematical models
Physics education
Problem-solving
Prompt Engineering
Symbolic Regression
Systematics
Undergraduate Learning
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Summary:Interleaved practice enhances the memory and problem-solving ability of students in undergraduate courses. We introduce a personalized learning tool built on a Large Language Model (LLM) that can provide immediate and personalized attention to students as they complete homework containing problems interleaved from undergraduate physics courses. Our tool leverages the dimensional analysis method, enhancing students' qualitative thinking and problem-solving skills for complex phenomena. Our approach combines LLMs for symbolic regression with dimensional analysis via prompt engineering and offers students a unique perspective to comprehend relationships between physics variables. This fosters a broader and more versatile understanding of physics and mathematical principles and complements a conventional undergraduate physics education that relies on interpreting and applying established equations within specific contexts. We test our personalized learning tool on the equations from Feynman's lectures on physics. Our tool can correctly identify relationships between physics variables for most equations, underscoring its value as a complementary personalized learning tool for undergraduate physics students.
DOI:10.1109/CAI59869.2024.00017