2023

Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Yan, Lixiang, Sha, Lele, Zhao, Linxuan et al.

Understand

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content.

While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations.
Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts.
To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks.

Built on

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Similar

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IEEE Transactions on Learning Technologies
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arXiv preprint arXiv:2112.04359
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Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A. et al. (2021) Ethical and social risks of harm from language models · 2021
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Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M. and Drachsler, H. (2021) Are we there yet?-a systematic literature review on chatbots in education · 2021
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Then

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In Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1
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Nature Machine Intelligence
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