Comparing human-made and AI-generated teaching videos: An experimental study on learning effects

IF 8.9 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Education Pub Date : 2024-09-21 DOI:10.1016/j.compedu.2024.105164
Torbjørn Netland, Oliver von Dzengelevski, Katalin Tesch, Daniel Kwasnitschka
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Abstract

In the age of generative AI, can teaching videos be efficiently and effectively generated by large language models? In this study, the authors used generative AI tools to develop four short teaching videos for a management course and then compared them with human-generated videos on the same subjects in an online experiment. In an across-subject experimental design, 447 participants completed two treatment conditions presenting different mixes of AI-generated and human-made videos. The participants were asked to rate their learning experiences after each video and had their learning outcomes tested in a multiple-choice exam at the end of the session (N = 1788 video treatments). The findings show that human-generated videos provided a statistically significant but small advantage to participants in terms of learning experience, indicating that the participants still prefer to be taught by human teachers. However, a comparison of exam results between the experimental groups implies that the participants eventually acquired knowledge about the content to a similar degree. Given these findings and the ease with which AI-generated teaching videos can be created, this study concludes that AI-generated teaching videos will likely proliferate.
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比较人工制作和人工智能生成的教学视频:学习效果实验研究
在生成式人工智能时代,大型语言模型能否高效生成教学视频?在这项研究中,作者使用生成式人工智能工具为一门管理课程制作了四个教学视频短片,然后在一项在线实验中将它们与人工生成的视频进行了比较。在跨受试者实验设计中,447 名参与者完成了两种处理条件,分别呈现了人工智能生成视频和人工制作视频的不同组合。参与者在观看完每段视频后都要对自己的学习体验进行评分,并在课程结束时进行选择题考试,测试他们的学习成果(N = 1788 次视频处理)。研究结果表明,人类生成的视频在学习体验方面给参与者带来了显著的统计学优势,但优势很小,这表明参与者仍然更喜欢由人类教师授课。不过,对各实验组考试成绩的比较表明,学员最终获得的内容知识程度相近。鉴于这些发现和人工智能生成教学视频的便捷性,本研究得出结论,人工智能生成的教学视频很可能会大量出现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Education
Computers & Education 工程技术-计算机:跨学科应用
CiteScore
27.10
自引率
5.80%
发文量
204
审稿时长
42 days
期刊介绍: Computers & Education seeks to advance understanding of how digital technology can improve education by publishing high-quality research that expands both theory and practice. The journal welcomes research papers exploring the pedagogical applications of digital technology, with a focus broad enough to appeal to the wider education community.
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