用AI制造不确定性，激发学生批判性思维。
Strategies for Creating Uncertainty in the AI Era to Trigger Students Critical Thinking: Pedagogical Design, Assessment Rubric, and Exam System
- 设计含AI局限性的学习任务，引导学生质疑与推理。
- 考试中控制AI输出，防止其直接给出正确答案。
- 开发可评估思维过程的AI辅助考评系统。
生成式AI让学生成绩看似正确却无真实理解，本文主张不禁止AI，而是利用其局限性制造不确定性，以激发批判性思维。基于认识论与批判性思维研究，提出围绕AI模型与教师自身局限设计教学活动与评估体系，鼓励学生推理、质疑并论证最终答案。通过控制考试中AI行为（如阻止直接回答或生成似是而非的错误回应），避免AI成为获取确定答案的捷径。为此，我们构建了MindMosaicAIExam系统，支持学生先提交初步答案，再批判性评估AI输出，并迭代优化推理过程。同时提供一套评估量规，用于分析系统收集的学生推理文本，衡量其批判性思维水平。
原文摘要 · Abstract (English)
Generative AI challenges traditional assessments by allowing students to produce correct answers without demonstrating understanding or reasoning. Rather than prohibiting AI, this work argues that one way to integrate AI into education is by creating uncertain situations with the help of AI models and using thinking-oriented teaching approaches, where uncertainty is a central pedagogical concept for stimulating students critical thinking. Drawing on epistemology and critical thinking research studies, we propose designing learning activities and assessments around the inherent limitations of both AI models and instructors. This encourages students to reason, question, and justify their final answers. We show how explicitly controlling AI behavior during exams (such as preventing direct answers or generating plausible but flawed responses) prevents AI from becoming a shortcut to certainty. To support this pedagogy, we introduce MindMosaicAIExam, an exam system that integrates controllable AI tools and requires students to provide initial answers, critically evaluate AI outputs, and iteratively refine their reasoning. We also present an evaluation rubric designed to assess critical thinking based on students reasoning artifacts collected by the exam system.
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