用X1-X2-X3三段式设计考试，让学生评估AI答案并展现真实学习能力。
Assessment Design in the GenAI Era: The X1-X2-X3 Assessment Pattern for Testing Students' AI Literacy, Learning Outcomes, and Reflection
- 采用X1-X2-X3三段式答题结构：引用答案、自主作答、评价AI输出。
- 通过反复测试与修订，确保题目无法被简单提示生成敷衍回答。
- 适合想培养学生AI素养而非惩罚使用AI的教师参考。
生成式人工智能（GenAI）对非监督在线考核的有效性构成挑战，尤其在技术类课程中，学生可轻易生成看似合理的答案。本文报告了一项大型二年级本科生数据库系统课程中，设计并实施的面向AI的评估实践。该设计包含两个核心部分：（1）三段式作答结构（X1-X2-X3），要求学生记录来源答案、自行作答，并评估来源输出；（2）面向AI的问题设计流程，将草稿题型在主流GenAI工具上进行压力测试，若通用提示即生成表面合格的答案，则予以修订。研究基于存档的评估材料、评分标准、规划记录、设计阶段的GenAI测试、练习作答数据、成绩记录及外部评审意见。其主要贡献是提出一种可复用的评估设计方法，而非声称具体学习成效提升。文章展示了该模式在迭代中的演化过程，说明其如何支持真实性评估、可见的AI素养、学生判断力和更透明的评分。为教师在日常教学中应对GenAI普及提供实用指导，重点在于测试学生的AI素养，而非惩罚使用AI的行为。
原文摘要 · Abstract (English)
Generative artificial intelligence (GenAI) has challenged the validity of unsupervised online assessment, especially in technical subjects where plausible answers can be produced with little effort. This paper reports lessons from designing and implementing an AI-aware, AI-testing assessment in a large second-year undergraduate database systems module. The design combined two linked elements: (1) a structured three-part response format (X1-X2-X3) in which students documented a sourced answer, produced their own answer, and evaluated the sourced output; and (2) an AI-aware question-design process in which draft tasks were stress-tested against contemporary GenAI tools and revised when generic prompting produced superficially adequate answers. The account draws on archived assessment materials, rubrics, planning records, design-time GenAI trials, practice-response data, attainment records, and external review comments. Its main contribution is a reusable assessment-design method rather than a claim of measured learning gains. We show how the pattern developed across iterations and how it can support authentic assessment, visible AI literacy, student judgement, and more transparent marking. The paper offers practical guidance for lecturers adapting assessment to routine GenAI use, focusing on testing AI literacy rather than penalising students for misconduct.
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