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针对医工交叉基础课程中(以高等数学为代表)医科与工科背景学习者并存、统一供给难以兼顾差异的矛盾,探讨人工智能驱动的个性化学习资源推荐与路径调度机制。在梳理数学焦虑、学习者画像、智能推荐与生成式人工智能教育应用等研究的基础上,构建“动机-焦虑-先验-行为”四维画像框架,提出推荐算法与教学规则(难度阶梯、形成性评价门控)相融合的推荐调度机制,以情境通道匹配背景差异、以评价反馈驱动资源重调度,形成“画像-推荐-学习-评价-画像更新”闭环,并给出四阶段实施路径与效果分析。为演示分析流程,本文按课程班额构造合成示例数据,完成画像差异与机制效果的描述统计与假设检验,所得统计量仅具方法学示例意义,正式结论以实测数据为准。国际学研究采用SATI文献题录信息统计分析工具统计分析我国核心期刊双师型队伍研究情况。
人工智能;个性化学习;学习者画像;学习资源推荐;医工交叉基础课程;高等数学
This study addresses the mismatch between heterogeneous medical- and engineering-background learners and uniform instruction in medicine-engineering interdisciplinary foundation courses (exemplified by advanced mathematics). Drawing on mathematics anxiety, learner profiling, intelligent recommendation, and generative AI research, it builds a four-dimensional learner-profile framework (motivation, math anxiety, prior knowledge, learning behavior) and a recommendation-and-scheduling mechanism fusing algorithms with pedagogical rules (difficulty laddering, formative-assessment gating), embedding evaluation feedback in a closed loop of profiling-recommendation-learning- evaluation-updating, and presents a four-stage implementation pathway and an outcome-analysis design. Synthetic example data at the planned class sizes are analyzed to demonstrate the statistical workflow; the statistics carry methodological illustration only, and formal conclusions await empirical data.
generative AI; medical students; fear of mathematics; case-based teaching; medical higher mathematics
施雯,邓镇,刘金荣,习佳宁. 人工智能驱动的医工交叉基础课程个性化学习资源推荐机制与实践[J]. 人文与社会科学学刊. 2026, 2 (10): 111-117. DOI: 10.70693/202609081619.
施雯, 邓镇, 刘金荣, 习佳宁. (2026). 人工智能驱动的医工交叉基础课程个性化学习资源推荐机制与实践. 人文与社会科学学刊, 2 (10), 111-117. https://doi.org/10.70693/202609081619