案例驱动的人工智能通识课程教学内容优化与实践探索

Optimization and Practical Exploration of Teaching Content for Case-Driven General Course on Artificial Intelligence

摘要: 为解决传统人工智能通识课程内容抽象、实践薄弱、跨学科与思政融合不足等问题，本研究聚焦课程教学内容优化，探索案例驱动教学法的应用路径。通过文献研究、案例分析与行动研究，构建“基础概念类–技术应用类–伦理社会类”三类案例体系，明确真实性、相关性、多样性、启发性的案例选择原则；重构“问题导向、跨学科融合、前沿融入”的知识体系，优化“基础–进阶–综合”三级实践教学内容，并通过案例渗透、实践体现、专题讨论实现思政元素深度融入。研究为非计算机专业人工智能通识课程教学提供了可行路径，未来将进一步完善分专业案例库与虚拟仿真实践平台，深化“AI + 思政”融合。

Abstract: To address issues such as abstract content, weak practical links, and insufficient integration of interdisciplinary perspectives and ideological and political elements in traditional general courses on artificial intelligence (AI), this study focuses on optimizing the course content and explores the application path of case-driven teaching method. Through literature research, case analysis, and action research, a three-category case system including “basic concept cases, technical application cases, and ethical and social cases” is constructed, and the case selection principles of authenticity, relevance, diversity, and inspiration are clarified. The study reconstructs a knowledge system featuring “problem orientation, interdisciplinary integration, and cutting-edge integration”, optimizes the “basic-advanced-comprehensive” three-level practical teaching content, and realizes the in-depth integration of ideological and political elements through case infiltration, practical reflection, and thematic discussion. This study provides a feasible path for the teaching of general AI courses for non-computer majors. In the future, efforts will be made to further improve the major-specific case database and virtual simulation practice platform, and deepen the integration of “AI + ideological and political education”.