Quantitative Evaluation of Using Large Language Models and Retrieval-Augmented Generation in Computer Science Education
Abstract
Generative artificial intelligence (GenAI) is transforming Computer Science education, and every instructor is reflecting on how AI will impact their courses. Instructors must determine how students may use AI for course activities and what AI systems they will support and encourage students to use. This task is challenging with the proliferation of large language models (LLMs) and related AI systems. The contribution of this work is an experimental evaluation of the performance of multiple open-source and commercial LLMs utilizing retrieval-augmented generation in answering questions for computer science courses and a cost-benefit analysis for instructors when determining what systems to use. A key factor is the time an instructor has to maintain their supported AI systems and the most effective activities for improving their performance. The paper offers recommendations for deploying, using, and enhancing AI in educational settings.
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