The Application of AI Chatbot System Based on CLIL Concept in the Teaching of Artificial Intelligence Courses

Abstract

The interdisciplinary nature of artificial intelligence courses forces non-computer science majors to contend with the simultaneous challenges of terminology comprehension and language cognition. To increase the efficiency of terminology teaching, this project develops and deploys an OpenAI-based AI chatbot teaching system that incorporates the concept of content and language integrated learning (CLIL). The system creates a dual-track “terminology layer-cognition layer” framework that includes term recognition, multi-level explanation (contextual examples and conceptual associations), task-driven dialogues, and conversation memory bank (CMB) modules. It then guides students through natural language interactions to master the core AI terms in context. The system’s effectiveness was confirmed in a controlled experiment with 98 participants (including computer and non-computer majors) separated into two groups: experimental (chatbot teaching) and control (conventional PPT teaching). In terms of terminology mastery, the experimental group’s posttest score (86.0 ± 5.33) was considerably higher than that of the control group (66.98 ± 5.6). Non-computer science major students showed a more significant improvement effect (83.29 ± 4.5 vs. 63.62 ± 4.68 for the control group). Non-computing students evaluated the clarity of systematic terminology explanation (4.33 ± 0.76) and the effectiveness of contextual assistance (4.21 ± 0.88) as the most important aspects of their learning experience. These experimental results show that the fusion AI chatbot teaching system developed in this study can improve teaching efficiency while effectively reducing cognitive load, and that the task-guided and immediate feedback mechanism can significantly increase students’ learning engagement.

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