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ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Written for the Research library (claude-opus-5-5) from the paper text. Original — osf.io/5er8f/download

Summary

This position paper asks how large language models such as GPT-3 and ChatGPT could change education and what risks come with them. It was written by an interdisciplinary group of 23 researchers from TU Munich, LMU Munich and the University of Tübingen. It is not an empirical study, so it introduces no dataset, model or benchmark. It first gives a short overview of the technology: transformers, self-attention, pre-training followed by fine-tuning, and few-shot adaptation. It then lists possible uses for learners at every level, from primary school to professional training, and for teachers, including personalised feedback, course planning, language teaching and help with writing. On the risk side, the authors cover bias, poor interpretability, unexpected brittleness, misuse, the need for continuous human oversight and unequal access. Their main argument is that students and teachers need new AI literacies, critical thinking and routine fact-checking, supported by clear strategies at the level of the education system. The paper matters as an early, widely cited framework for using LLMs responsibly in schools and universities.

Key points

  • Learner-side opportunities are grouped by audience: elementary, middle/high school, university, group & remote learning, learners with disabilities (combined with speech-to-text and text-to-speech), and professional training (models fine-tuned on legal, medical or IT corpora).
  • Teacher-side uses include personalised learning, generating a course syllabus from a set of source documents, quizzes and practice problems, support for language classes, and help with research, writing and grading feedback.
  • Proposed fixes for the models' poor adaptability: adaptive learning based on student data, aligning output with the curriculum, multimodal teaching, hybrid human–LLM setups, fact-checking protocols, and openness about what the model can and cannot do.
  • The authors flag that LLM research focuses mainly on English, so non-English speakers may get less from these tools. This bears directly on Ukrainian-language NLP.
  • They argue that cost barriers to access may need government regulation, and they back UNESCO's Education 2030 Agenda call that AI should not widen educational divides. The paper reports no experimental numbers.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Education

Cited by

207 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 199 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.35542/osf.io/5er8f

@misc{kasneci2023chatgpt,
  title = {{ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education}},
  author = {Kasneci, Enkelejda and Seßler, Kathrin and Küchemann, Stefan and Bannert, Maria and Dementieva, Daryna and Fischer, Frank and Gasser, Urs and Groh, Georg and Günnemann, Stephan and Hüllermeier, Eyke and Krusche, Stephan and Kutyniok, Gitta},
  year = {2023},
  doi = {10.35542/osf.io/5er8f},
  url = {https://doi.org/10.35542/osf.io/5er8f}
}

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