生成式人工智能支持的数据结构教学改革

Exploring Teaching Reform in the Data Structures Course with Generative Artificial Intelligence

摘要: 针对生成式人工智能辅助编程条件下数据结构课程中代码完成与概念理解脱节、算法分析缺少验证依据、学习评价难以识别独立能力等问题，本文围绕绪论、算法分析、线性表、栈与队列设计递进式教学任务，将独立建模、人机协同诊断、测试解释和约束变化迁移贯穿教学过程。以有界循环队列为例，给出容量约定、错误诊断、状态追踪及评价依据，并通过有限操作序列枚举检验示例程序与诊断用例。该方案发挥生成式人工智能在反馈生成、错误诊断和测试辅助等方面的作用，并将工具使用记录与学生独立解释共同纳入评价，为前四章教学资源优化和后续课堂验证提供依据。

Abstract: To address the problems in the Data Structures course under generative artificial intelligence-assisted programming, including the disconnection between code completion and conceptual understanding, the lack of verification basis for algorithm analysis, and the difficulty in identifying students’ independent competence in learning assessment, this study designs progressive teaching tasks for the units of introduction, algorithm analysis, linear lists, stacks and queues, and integrates independent modeling, human-AI collaborative diagnosis, test-based explanation and transfer under changing constraints throughout the teaching process. Taking the bounded circular queue as an example, this paper specifies capacity conventions, fault diagnosis, state tracing and assessment evidence, and verifies sample implementations and diagnostic test cases via finite enumeration of operation sequences. The proposed scheme leverages the strengths of generative artificial intelligence in feedback generation, fault diagnosis and test assistance, and incorporates both tool usage records and students’ independent explanations into the assessment framework, providing a foundational basis for optimizing teaching resources of the first four chapters and carrying out subsequent classroom validation.