The Design and Development of a Computer Science Teaching Assistant Agent Based on Large Language Models

Abstract

The rapid development of artificial intelligence and large language models has brought new opportunities and challenges to information technology education. Aiming at the problems such as inaccurate feedback when information technology teachers apply general artificial intelligence, this study constructed the technical architecture of the teaching assistant agent based on the ReAct mechanism and developed an agent for teaching assistance with the help of Retrieval Enhancement Generation (RAG) technology. The accuracy of the feedback is evaluated by comparing the performance of the teaching assistant agent and the general large model in terms of precision and recall rate. The research results show that the information technology subject teaching assistant agent significantly outperforms the general large model in terms of feedback accuracy. Its precision and recall rate both demonstrate a higher level, especially in tasks such as teaching design and test question generation, where it has obvious advantages, effectively compensating for the limitations of the general large model in the correspondence and output accuracy of textbook content.