  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.
