发表机构
The School of Electrical and Computer Engineering, The University of Sydney（悉尼大学电气与计算机工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对软件工程教育中教育者难观测学生与GenAI互动过程的问题，提出结合执行追踪与认知审计的AI Journal框架，可轻量、模型无关地呈现学习过程，推动评估从关注成果转向判断能力与负责任AI实践。
AI 中文摘要
生成式AI（GenAI）正日益融入软件工程教育，为需求开发、设计探索、文档编写和原型制作等活动提供支持。然而，教育者通常只能看到最终成果，对学生在学习过程中如何评估、验证和完善AI生成的输出了解有限，这给评估判断能力和负责任的AI辅助实践带来了挑战。本文提出了AI Journal（AI日志），这是一个结构化反思框架，旨在让一年级软件工程教育中的学生与GenAI的互动过程可见。该框架结合了执行追踪与认知审计：执行追踪记录提示词、输出、意图和互动背景；认知审计捕捉验证策略、干预决策、置信度判断、关键学习时刻以及对AI辅助工作的反思。AI Journal在第一学期的软件工程课程中部署后，能够揭示仅通过基于成果的评估无法观察到的学生学习方面。初步观察显示，学生在验证实践、干预策略和对AI辅助工作的认知上存在差异。关键学习时刻常出现在学生评估上下文适用性、可行性和需求一致性时，而非发现明显错误时。AI Journal展示了一种实用、轻量且模型无关的方法，可让AI辅助学习过程可见。通过突出验证、干预和反思，它将注意力从以产品为中心的评估转向评估判断能力和负责任的AI辅助实践。
英文摘要
Generative AI (GenAI) is increasingly embedded in software engineering education, supporting activities such as requirements development, design exploration, documentation, and prototyping. However, educators often have visibility only into final artefacts, with limited insight into how students evaluate, verify, and refine AI-generated outputs during the learning process. This creates challenges for assessing evaluative judgement and responsible AI-assisted practice. This paper introduces the AI Journal, a structured reflection framework designed to make student-GenAI interaction visible in first-year software engineering education. The framework combines execution tracking, which records prompts, outputs, intent, and interaction context, with cognitive auditing, which captures verification strategies, intervention decisions, confidence judgements, critical learning moments, and reflections on AI-supported work. Deployed in a first-semester software engineering course, the AI Journal enabled visibility into aspects of student learning not observable through artefact-based assessments alone. Preliminary observations suggested variation in verification practices, intervention strategies, and perceptions of AI-supported work. Critical learning moments frequently occurred when students evaluated contextual suitability, feasibility, and requirements alignment rather than identifying obvious errors. The AI Journal demonstrates a practical, lightweight, and model-agnostic approach for making AI-assisted learning processes visible. By foregrounding verification, intervention, and reflection, it shifts attention from product-focused assessment toward evaluative judgement and responsible AI-assisted practice.
Comments9 pages, 2 figures, Accepted for publication in the Proceedings of the 37th Annual Conference of the Australasian Association for Engineering Education (AAEE 2026)
