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ChatGPT for good? On opportunities and challenges of large language models for education

课程 计算机科学 领域(数学) 工程伦理学 知识管理 管理科学 心理学 教育学 工程类 数学 纯数学
作者
Enkelejda Kasneci,Kathrin Seßler,Stefan Küchemann,Maria Bannert,Daryna Dementieva,Frank Fischer,Urs Gasser,Georg Groh,Stephan Günnemann,Eyke Hüllermeier,Stephan Krusche,Gitta Kutyniok,Tilman Michaeli,Claudia Nerdel,Jürgen Pfeffer,Oleksandra Poquet,Michael Sailer,Albrecht Schmidt,Tina Seidel,Matthias Stadler
出处
期刊:Learning and Individual Differences [Elsevier BV]
卷期号:103: 102274-102274 被引量:2279
标识
DOI:10.1016/j.lindif.2023.102274
摘要

Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This commentary presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.
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