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

Abstract

<title>Abstract</title> Introduction 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. 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 &lt; 0.05). The experimental group also exhibited markedly higher levels of self-directed learning ability and learning engagement level than the control group (p &lt; 0.05). Students expressed overall satisfaction with the "AI+Knowledge Graph" teaching model based on the ARCS motivation model and provided positive feedback. Conclusion This study demonstrated that the "AI+Knowledge Graph" teaching model based on the ARCS motivation model effectively enhances students' academic performance, self-directed learning ability, and learning engagement level, exhibiting significant advantages in teaching Integrated Chinese and Western Oncology. Future research may further explore the applicability of this model across different disciplines and teaching environments, while also examining its long-term educational effects and technical optimisation pathways.

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