CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education

TL;DR

The paper tackles the challenge of scalable, quality CS tutoring amid growing class sizes while addressing the risks of LLMs such as overreliance and code miscomprehension. It introduces CourseAssist, an LLM-based tutoring system that combines retrieval-augmented generation, user intent classification, and question decomposition to align AI responses with course materials and learning objectives. The authors validate CourseAssist against a GPT-4 baseline using a 50-question Piazza dataset from a programming languages course, deploy it in 6 CS courses, and gather qualitative feedback from 20 students, highlighting improved accessibility and faster feedback loops. Results indicate that CourseAssist yields higher usefulness, accuracy, and pedagogical appropriateness, demonstrating potential for impact as a scalable, course-specific learning assistant in computer science education.

Abstract

The growing enrollments in computer science courses and increase in class sizes necessitate scalable, automated tutoring solutions to adequately support student learning. While Large Language Models (LLMs) like GPT-4 have demonstrated potential in assisting students through question-answering, educators express concerns over student overreliance, miscomprehension of generated code, and the risk of inaccurate answers. Rather than banning these tools outright, we advocate for a constructive approach that harnesses the capabilities of AI while mitigating potential risks. This poster introduces CourseAssist, a novel LLM-based tutoring system tailored for computer science education. Unlike generic LLM systems, CourseAssist uses retrieval-augmented generation, user intent classification, and question decomposition to align AI responses with specific course materials and learning objectives, thereby ensuring pedagogical appropriateness of LLMs in educational settings. We evaluated CourseAssist against a baseline of GPT-4 using a dataset of 50 question-answer pairs from a programming languages course, focusing on the criteria of usefulness, accuracy, and pedagogical appropriateness. Evaluation results show that CourseAssist significantly outperforms the baseline, demonstrating its potential to serve as an effective learning assistant. We have also deployed CourseAssist in 6 computer science courses at a large public R1 research university reaching over 500 students. Interviews with 20 student users show that CourseAssist improves computer science instruction by increasing the accessibility of course-specific tutoring help and shortening the feedback loop on their programming assignments. Future work will include extensive pilot testing at more universities and exploring better collaborative relationships between students, educators, and AI that improve computer science learning experiences.