Design and Evaluation Attributes for Scalable, Cost-Effective Personalization of LLM Tutors in Programming Education

This paper examines the design and evaluation of Large Language Model (LLM) tutors for Python programming, focusing on personalization that accommodates diverse student backgrounds. It highlights the challenges faced by socioeconomically disadvantaged students in computing courses and proposes LLM tutors as a solution to provide inclusive educational support. The study explores two LLM tutors, Khanmigo and CS50.ai, assessing their ability to offer personalized learning experiences. By employing a focus group methodology at a public minority-serving institution, the research evaluates how these tutors meet varied educational goals and adapt to students’ diverse needs. The findings underscore the importance of advanced techniques to tailor interactions and integrate programming tools based on students' progress. This research contributes to the understanding of educational technologies in computing education and provides insights into the design and implementation of LLM tutors that effectively support equitable student success.