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教育数字化与医工融合背景下,生成式AI为医用高等数学案例教学提供了情境生成、任务变式、过程支架与即时反馈等新支持,但医科生数学畏难仍受课程价值认知偏弱、案例链条不足与人机协同失衡等因素制约。文章围绕现实困境、形成原因与优化路径展开分析,提出医学场景重塑、分层案例拓展、推理校验强化、过程评价优化与资源共建保障等对策。
生成式AI;医科生;数学畏难;案例教学;医用高等数学
Against the backdrop of educational digitalization and the integration of medicine and engineering, generative AI provides new support for case-based teaching in medical higher mathematics, including scenario generation, task variation, process scaffolding, and immediate feedback. However, medical students’ fear of mathematics is still constrained by weak recognition of course value, insufficient case chains, and imbalanced human–AI collaboration. This article analyzes the practical difficulties, underlying causes, and optimization paths, and proposes countermeasures including medical scenario reconstruction, layered case expansion, strengthened reasoning verification, improved process evaluation, and resource co-construction.
generative AI; medical students; fear of mathematics; case-based teaching; medical higher mathematics
施雯,魏悦姿,习佳宁. 面向医科生数学畏难的生成式AI案例教学研究[J]. 中国现代教育学报. 2026, 2 (5): 41-48. DOI: 10.70693/202609027344.
施雯, 魏悦姿, 习佳宁. (2026). 面向医科生数学畏难的生成式AI案例教学研究. 中国现代教育学报, 2 (5), 41-48. https://doi.org/10.70693/202609027344