Kestin Greg, Miller Kelly, Klales Anna, Milbourne Timothy, Ponti Gregorio
Department of Physics, Harvard University, 17 Oxford Street, Cambridge, MA, 02138, USA.
School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA, 02138, USA.
Sci Rep. 2025 Jun 3;15(1):17458. doi: 10.1038/s41598-025-97652-6.
Advances in generative artificial intelligence show great potential for improving education. Yet little is known about how this new technology should be used and how effective it can be compared to current best practices. Here we report a randomized, controlled trial measuring college students' learning and their perceptions when content is presented through an AI-powered tutor compared with an active learning class. The novel design of the custom AI tutor is informed by the same pedagogical best practices as employed in the in-class lessons. We find that students learn significantly more in less time when using the AI tutor, compared with the in-class active learning. They also feel more engaged and more motivated. These findings offer empirical evidence for the efficacy of a widely accessible AI-powered pedagogy in significantly enhancing learning outcomes, presenting a compelling case for its broad adoption in learning environments.
生成式人工智能的进展显示出改善教育的巨大潜力。然而，对于如何使用这项新技术以及与当前最佳实践相比其效果如何，人们知之甚少。在此，我们报告一项随机对照试验，该试验测量了大学生在通过人工智能驱动的辅导工具呈现内容时的学习情况及其看法，并与主动学习课堂进行了比较。定制人工智能辅导工具的新颖设计借鉴了与课堂教学相同的最佳教学实践。我们发现，与课堂主动学习相比，学生使用人工智能辅导工具时能在更短时间内学到显著更多的知识。他们也感觉参与度更高、积极性更强。这些发现为一种广泛可用的人工智能驱动教学法在显著提高学习成果方面的有效性提供了实证证据，有力地证明了其在学习环境中广泛采用的合理性。
