AI 中文总结
研究探讨人工智能辅助教育中，学生学习行为异质性、谁受益及如何受益。通过对318名大学生的实验发现，学习行为与成果紧密相关，积极行为更有利，且行为差异与学习者特征有关，使用方式是背景与学习收获关联的关键途径，为理解人工智能辅助教育提供新视角。
AI 中文摘要
生成式人工智能在教育中已被证明且可能越来越有用，但现有人工智能教育研究对其平均效果的证据不一致。更广泛地说，先前教育技术研究表明平均效果常掩盖学生群体的显著异质性。本研究探讨学生使用人工智能时学习行为的异质性、哪些学生受益以及学习者特征和学习行为如何塑造这些模式。为此招募318名大学生参与长达125分钟的结构化学习实验。结果表明学生的学习行为与学习成果密切相关，积极主动和批判性参与的行为与更好的表现相关。这些行为差异与学习者特征有关，来自排名较高大学和有更多先验知识的学生受益更多。考虑学习行为会削弱或消除学习者特征与学习成果之间的关联，表明学生使用人工智能的方式是背景差异与学习收获相关的关键途径。这项工作通过展示学习者特征和学习行为的差异如何影响谁从人工智能支持的学习中受益，加深了对人工智能在教育中辅助作用的理解，可为教育工作者和学生更好地将人工智能融入教育实践提供帮助。
英文摘要
Generative AI has been found, and will likely be found increasingly, useful in education. However, existing AI-for-education studies provide inconsistent evidence on its average effects. More broadly, research on prior educational technologies shows that average effects often mask substantial heterogeneity across student populations. Motivated by this evidence, this study examines heterogeneity in students' learning behavior with AI, which students benefit from AI assistance, and how learner profiles and learning behavior shape these patterns. To this end, we recruited 318 university students to participate in structured learning experiments lasting up to 125 minutes. Our findings indicate that students' learning behavior is strongly associated with learning outcomes, with behaviors characterized by proactive and critical engagement, rather than limited engagement, associated with significantly better performance. These behavioral differences are related to learner profiles, with students from higher-ranking universities and those with greater prior knowledge tending to benefit more, consistent with their greater likelihood of adopting proactive interaction strategies. Accounting for learning behavior substantially weakens or eliminates the associations between learner profiles and learning outcomes, suggesting that how students use AI is a key pathway through which background differences are linked to learning gains. Overall, this work provides a deeper understanding of AI assistance in education by showing how differences in learner profiles and learning behavior shape who benefits from AI-supported learning. These insights can help educators and students better navigate and integrate AI into educational practices.
