Construction and Evaluation of an "AI+Knowledge Graph" Teaching Model Based on the ARCS Motivation Model: A Case Study of Integrated Chinese and Western Oncology (Preprint)

BACKGROUND The integration of artificial intelligence（AI） technology and knowledge graphs（KG） in education offers novel possibilities for pedagogical innovation. This study aims to construct and evaluate the application effectiveness of an "AI+Knowledge Graph" teaching model based on the ARCS motivation model in teaching Integrated Chinese and Western Oncology, exploring its role in enhancing students' academic performance, self-directed learning ability, and learning engagement level. OBJECTIVE Construction and Evaluation of an "AI+Knowledge Graph" Teaching Model Based on the ARCS Motivation Model METHODS One hundred undergraduate medical students were randomly allocated to an experimental group (n=50) and a control group (n=50). The experimental group adopted the "AI+Knowledge Graph" teaching model based on the ARCS motivation model, while the control group adopted the traditional teaching model. Differences in educational outcomes were systematically assessed using examinations, self-directed learning scales, learning engagement scales, and satisfaction questionnaires. RESULTS The experimental group demonstrated significant superiority in total score, final exam score, usual performance, and all sub-dimensions (learning and thinking, collaboration and innovation, diagnosis and summary) compared to the control group (p