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

A position paper examining opportunities and challenges of large language models for education from student and teacher perspectives.

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

By Enkelejda Kasneci, Kathrin Seßler, S. Küchemann et al.Learning and Individual Differences
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This position paper discusses the potential benefits and challenges of educational applications of large language models, viewed from both student and teacher perspectives. After briefly reviewing the current state of large language models and their applications, it highlights how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. The authors treat the technology as here to stay despite critical views and bans in some communities.

On the challenges side, the paper argues that using large language models in education requires teachers and learners to develop competencies and literacies to understand both the technology and its limitations and unexpected brittleness. It calls for clear strategies within educational systems and a pedagogical approach centered on critical thinking and fact checking, while noting risks such as output bias, the need for continuous human oversight, and potential misuse. The authors conclude that, handled sensibly, these challenges can themselves become opportunities to teach students about the biases and risks of AI, and offer recommendations for responsible and ethical use.

Abstract

This position paper examines the opportunities and challenges of large language models in education, from student and teacher perspectives. It highlights how such models can create educational content, improve student engagement and interaction, and personalize learning. It argues teachers and learners must build competencies to grasp the technology's limitations and brittleness, aided by strategies for critical thinking and fact checking. Challenges such as output bias, human oversight, and misuse are discussed, with recommendations for responsible, ethical use.

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large language modelsChatGPTeducationpersonalized learningAI ethics
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