以判断为中心的软件工程教育：AI增强学习的后炒作回顾与框架
Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning
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中文总结 AI 辅助
本文回顾2023-2026年研究，提出以判断为中心的AI增强软件工程教育框架，包含五个集成层级和四项证据义务，以应对理解债务风险。
中文摘要 AI 辅助
生成式人工智能已从颠覆性的新奇事物转变为软件开发与计算教育流程中的常规组成部分，而软件代理正开始跨仓库、命令行、浏览器、测试及其他工具发挥作用。教育问题已不再是学生是否应被允许生成代码，而是软件工程（SE）项目能否在培养学生负责任地与日益强大的AI系统协作的同时，保持并评估人类理解。本文呈现了一项结构化的整合性回顾，涵盖2023年至2026年9月23日期间的研究与实践，并辅以关于AI素养、技术债务和人机协作的既有工作。证据支持一个条件性结论：GenAI能改善对解释、反馈、实践和短期任务完成的获取，但学习成果取决于先前知识、支架、验证、任务设计和评估。我们考察了学生的求助行为与作者身份、教师的评估与政策需求，以及当生成的工件跨仓库、团队、架构和部署系统持续存在时所产生的更广泛的软件工程风险。我们扩展了理解债务的概念：当AI辅助生产超过学习者或团队解释、测试、修改和论证所生成软件的能力时，产生的延迟学习与维护成本。随后，我们将AI增强软件工程教育（AASEE）框架细化为五个非线性集成层级和四项跨领域证据义务：解释、验证、修改和说明。该框架将委派与适合其后果的证据、治理和恢复机制联系起来。
英文摘要
Generative artificial intelligence has moved from a disruptive novelty to a recurring part of software-development and computing-education workflows, while software agents are beginning to act across repositories, command lines, browsers, tests, and other tools. The educational problem is no longer whether students should be allowed to generate code, but whether software-engineering (SE) programs can preserve and assess human understanding while preparing students to work responsibly with increasingly capable AI systems. This paper presents a structured integrative review of research and practice from 2023 through 23 September 2026, supplemented by established work on AI literacy, technical debt, and human-AI collaboration. The evidence supports a conditional conclusion: GenAI can improve access to explanations, feedback, practice, and short-term task completion, but learning outcomes depend on prior knowledge, scaffolding, verification, task design, and assessment. We examine student help-seeking and authorship, faculty assessment and policy demands, and the wider SE risks created when generated artifacts persist across repositories, teams, architectures, and deployed systems. We extend the concept of comprehension debt: the deferred learning and maintenance cost that arises when AI-assisted production outpaces a learner's or team's ability to explain, test, modify, and justify the resulting software. We then refine the AI-Augmented Software Engineering Education (AASEE) framework into five non-linear integration levels and four cross-cutting evidence obligations: explain, verify, modify, and account. The framework links delegation to evidence, governance, and recovery mechanisms appropriate to its consequences.
发表机构
- Ontario Tech University（安大略理工大学）
机构由 AI 辅助整理，请以论文原文为准。
