用游戏项目实证AI编程工具在教学中的应用效果
Learning to Code with Context: A Study-Based Approach
- 通过学生协作开发游戏项目，研究AI工具在开发各阶段的使用方式
- 发现代码生成类任务中AI最有效，但存在上下文理解偏差问题
- 提出本地化部署的RAG系统，支持项目上下文感知的AI辅助
生成式AI工具的快速发展正在改变软件开发方式。因此，软件工程教育必须调整，使学生不仅掌握传统开发方法，还能有意义且负责任地使用这些新技术。项目制课程为探索和评估AI辅助在真实开发实践中的融合提供了有效环境。本文介绍了一项大学编程项目中的教学方法与用户研究，学生协作开发计算机游戏。研究调查了参与者在软件开发不同阶段如何使用生成式AI工具，识别出此类工具最有效的任务类型，并分析学生遇到的挑战。基于这些发现，我们进一步考察了一种面向代码仓库、本地部署的大语言模型（LLM）助手，该系统采用检索增强生成（RAG）技术，将响应锚定在相关文档和源代码中，从而实现对模型行为、参数敏感性和常见失效模式的定性分析。研究结果深化了对教育场景中上下文感知型AI支持的理解，为未来将基于AI的辅助集成到软件工程课程提供了依据。
原文摘要 · Abstract (English)
The rapid emergence of generative AI tools is transforming the way software is developed. Consequently, software engineering education must adapt to ensure that students not only learn traditional development methods but also understand how to meaningfully and responsibly use these new technologies. In particular, project-based courses offer an effective environment to explore and evaluate the integration of AI assistance into real-world development practices. This paper presents our approach and a user study conducted within a university programming project in which students collaboratively developed computer games. The study investigates how participants used generative AI tools throughout different phases of the software development process, identifies the types of tasks where such tools were most effective, and analyzes the challenges students encountered. Building on these insights, we further examine a repository-aware, locally deployed large language model (LLM) assistant designed to provide project-contextualized support. The system employs Retrieval-Augmented Generation (RAG) to ground responses in relevant documentation and source code, enabling qualitative analysis of model behavior, parameter sensitivity, and common failure modes. The findings deepen our understanding of context-aware AI support in educational software projects and inform future integration of AI-based assistance into software engineering curricula.
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