让AI主动犯错，反而帮学生更好理解物理概念
From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions
- 设计会犯错的AI同伴，引导学生通过对话纠正常见物理误解
- 实验组成绩平均提升10.5个百分点，纠错率超20个百分点
- 即使AI答错，学生也能从中获益，适合想培养批判思维者
生成式AI有潜力改变教育的个性化与可及性，但其准确性与对学生独立思考能力的培养引发担忧。本研究设计了一位会犯错的AI同伴，帮助学生纠正牛顿力学中的基础误解。不同于追求高准确率的权威导师模式，我们明确告知学生该AI最多有40%的回答错误。在165名学生的随机对照试验中，与讨论物理史的对照组相比，与AI同伴进行针对性对话的学生，后测成绩平均高出10.5个百分点，标准化增益超过20个百分点。91%的互动被评价为有帮助。通过对比前后测同概念题目表现及专家标注的交互记录，初步发现成绩提升不依赖于AI回答的正确性。未来研究可进一步探索此模式对学习方式的深远影响。
原文摘要 · Abstract (English)
Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accuracy and helping students become independent critical thinkers. In this study, we designed a helpful AI "Peer" to help students correct fundamental physics misconceptions related to Newtonian mechanic concepts. In contrast to approaches that seek near-perfect accuracy to create an authoritative AI tutor or teacher, we directly inform students that this AI can answer up to 40% of questions incorrectly. In a randomized controlled trial with 165 students, those who engaged in targeted dialogue with the AI Peer achieved post-test scores that were, on average, 10.5 percentage points higher - with over 20 percentage points higher normalized gain - than a control group that discussed physics history. Qualitative feedback indicated that 91% of the treatment group's AI interactions were rated as helpful. Furthermore, by comparing student performance on pre- and post-test questions about the same concept, along with experts' annotations of the AI interactions, we find initial evidence suggesting the improvement in performance does not depend on the correctness of the AI. With further research, the AI Peer paradigm described here could open new possibilities for how we learn, adapt to, and grow with AI.
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