用AI提升软件需求质量教育，设计引导学生合理使用工具。
Integrating AI into Requirements Quality Learning in Software Engineering Education: A TPACK-Guided Empirical Study
- 基于TPACK框架设计任务，引导学生有选择地使用AI辅助分析与评估。
- 学生对可测试性等具体维度理解提升明显，谈判性改善不一。
- 适合教育研究者与课程设计者参考，推动负责任的AI教学实践。
生成式人工智能在软件工程实践中的快速应用，对概念密集型课程如需求工程（RE）的教育提出了教学法基础的支持需求。本研究考察了在硕士级需求质量分析作业中，基于TPACK框架整合多智能体AI工具的效果。采用混合方法设计（N=100；分析72份提交），探讨结构化任务设计如何影响学生对AI的使用方式、对用户故事质量标准的理解，以及对AI优劣势的认知。结果表明，学生主要将AI作为分析与评估的辅助工具而非自动化手段，对价值表达和可测试性等结构化维度的改进最显著，而谈判性维度效果参差。学生表现出条件性信任，主动优化输出，并更关注质量标准，但存在中等程度的易用性挑战。研究显示，基于TPACK的支架设计可使AI能力与教学目标及内容相匹配，为需求工程教育中的负责任AI整合提供设计指引。
原文摘要 · Abstract (English)
The rapid adoption of generative Artificial Intelligence (AI) in software engineering (SE) practice creates a need for pedagogically grounded approaches to AI integration in SE education, especially in conceptually intensive subjects such as requirements engineering (RE). This study examines a TPACK-guided integration of a multi-agent AI tool into a master-level RE assignment on requirements quality analysis. Using a mixed-methods design (N=100; 72 submissions analysed), we examine how structured assignment design shaped students' AI use, affected their understanding of user story quality criteria, and influenced their perceptions of AI's benefits and limitations. Results show that students used the AI tool selectively, mainly as support for analysis and evaluation rather than automation. Alignment improvements were most evident for structurally concrete requirements quality dimensions, such as value articulation and testability, while negotiability showed mixed effects. Students reported conditional trust, active refinement, and increased awareness of quality criteria, alongside moderate usability challenges. The findings show that TPACK-guided scaffolding can align AI affordances with pedagogical goals and RE content, offering design guidance for responsible AI integration in RE education.
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