生成式AI在计算机专业教学中的融合模式与伦理挑战研究——以数据结构课程为例

Research on the Integration Model and Ethical Challenges of Generative AI in Computer Science Education—A Case Study of the Data Structure Course

摘要: 生成式人工智能的迅速发展正引发计算机专业教育的范式重构。本文以计算机学科核心基础课数据结构为研究对象，深入剖析了该课程“高抽象、强逻辑、重实践”的教学特点及其长期存在的“学生理解困难、教师因材施教难、理论与实践脱节”等问题。在此基础上，系统构建了一个生成式AI深度融入数据结构课程教学的“三维四阶”融合模式框架，涵盖教学内容、教学过程与教学评价三个维度，以及从工具辅助到思维重构的四个进阶阶段。本文结合二叉树旋转、图算法优化等典型案例，详细阐述了基于生成式AI的启发式教学设计、个性化学习路径生成及智能实践环境构建等具体实践方案。同时，本文还讨论了融合过程中衍生的学术诚信边界模糊、模型“幻觉”误导认知、数据隐私与算法偏见、以及学生批判性思维与元认知能力弱化四大伦理挑战。最终，提出了以“人机协同、智能增强”为核心，涵盖“教学范式重塑、师生数字素养提升、制度与技术保障”的综合性应对策略。本研究旨在为人工智能时代计算机专业核心课程的教学改革提供系统的理论参考、可操作的实践路径与系统性的伦理审视。

Abstract: The rapid development of generative artificial intelligence (GenAI) is driving a paradigm shift in computer science education. This paper focuses on the core foundational course of Data Structures in computer science, analyzing its teaching characteristics—characterized by “high abstraction, strong logic, and emphasis on practice”—as well as persistent challenges, including “students’ difficulties in understanding, challenges in implementing individualized teaching, and disconnection between theory and practice”. Building on this analysis, the paper systematically constructs a “Three-Dimensions, Four-Stages” integration model framework for deeply incorporating GenAI into Data Structures teaching. This framework spans three dimensions—teaching content, teaching process, and teaching evaluation—and four progressive stages, ranging from tool assistance to cognitive restructuring. Drawing on typical cases such as binary tree rotation and graph algorithm optimization, the paper elaborates on practical implementation plans, including heuristic instructional design based on GenAI, personalized learning path generation, and the construction of intelligent practical environments. Furthermore, the paper addresses four ethical challenges arising from this integration: the blurring of academic integrity boundaries, the risk of cognitive misguidance due to model “hallucinations”, concerns over data privacy and algorithmic bias, and the potential weakening of students’ critical thinking and metacognitive abilities. Finally, the paper proposes comprehensive countermeasures centered on “human-computer collaboration and intelligence enhancement”, encompassing “teaching paradigm transformation, enhancement of digital literacy among educators and learners, and institutional and technical safeguards”. This study aims to provide systematic theoretical insights, actionable implementation pathways, and a comprehensive ethical framework for reforming core computer science courses in the era of artificial intelligence.