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Large language models for education: A survey and outlook

Paper · 2024 · Wang

Summary

This survey, by Shen Wang and colleagues (Squirrel AI, Michigan State University, Zhejiang Normal University, UIC; arXiv 2403.18105), maps how large language models are used in education from a technology perspective. The authors argue that earlier reviews were mostly opinion- or interview-based and catalogued ChatGPT use cases without organizing the underlying methods. To fill that gap, they propose a taxonomy covering:

  • student support,
  • teacher support,
  • adaptive learning,
  • commercial tools.

They also collect public datasets and benchmarks and discuss deployment risks. Examples of these risks include plagiarism, biased outputs, overreliance on models and unequal access for speakers of languages other than English.

The review finds strong LLM performance on problem solving and error correction across subjects. However, controlled studies show that model-written hints and explanations still trail those written by teachers. The paper ends with a research agenda. For multilingual communities, including Ukrainian-language projects, the call for culturally aware, non-English educational models is especially relevant.

Key points

  • Student-side taxonomy: The survey splits support for learners into three areas:
    • question solving, including Chain-of-Thought prompting, tool-augmented reasoning, external verifiers and multi-agent setups such as AutoGen;
    • error correction;
    • "confusion helper" hint generation.
  • Teacher-side work: Teacher support includes question generation, for example GPT-4 producing Python MCQs aligned with learning objectives, as well as grading support.
  • Error correction examples:
    • GrammarGPT fine-tunes open LLMs on mixed human/ChatGPT annotations to correct native Chinese grammar errors.
    • MMAPR uses Codex to repair student Python assignments, fixing more programs with smaller patches than the previous state-of-the-art repair engine.
  • Limits of model-written guidance: In Pardos & Bhandari's pre/post-test study, ChatGPT algebra hints produced smaller learning gains than hints from human tutors.
  • Future directions:
    • aligning models with pedagogy;
    • multi-agent education systems, such as grader and critic agents for scoring;
    • multimodal and multilingual support;
    • edge deployment for offline and private use;
    • efficient training of subject-specific models;
    • ethics and privacy frameworks.

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

 Institutions

🎓University of Illinois ChicagoChicago · United States70 works · 20,363 citations🎓Michigan State UniversityEast Lansing · United States30 works · 1,375 citations🎓Zhejiang Normal UniversityJinhua · China5 works · 117 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksInformation Systems · 1,730 worksMedicine · 275 worksHealth Informatics · 214 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Wang. 2024. Large language models for education: A survey and outlook. arXiv (Cornell University).

The paper itself, not this page. BibTeX from the library's record of the work. arXiv 2403.18105

@misc{wang2024large,
  title = {{Large language models for education: A survey and outlook}},
  author = {Wang},
  year = {2024},
  eprint = {2403.18105},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2403.18105}
}

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