Multi-Text-Reading References Recommendation Method of Learning Traditional Chinese Literary Using Large Language Model AI Assistant

Large Language Model (LLM) based AI assistants are being widely applied in the academic field of education. This article aims to address the difficulties encountered by vocational college students in learning classical Chinese literature, for they may easily lose their interest and motivation while dealing with such difficult grammar and semantics. Considering of our teaching objectives and educational intentions, based on the cultural and Interesting characteristics of classical Chinese historical biographical literature works, and exploiting advantages of multi-text-reading teaching and learning instructions, we use the ChatGPT-like LLM AI assistants’ abilities of natural language understanding, logical reasoning, and content generation to design variables, rules, and algorithms for a multi-text-reading reading literature recommendation model for teaching and learning of classical Chinese historical biographical literature works from seven dimensions. A chain prompt engineering template is also designed for the ChatGPT-like LLMs. The experimental results show that the recommendation model and template we designed have a high degree of fit with our teaching objectives.