  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.
