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Position: LLMs Can be Good Tutors in English Education

Paper · 2025 · Jingheng Ye, Shen Wang, Deqing Zou et al.

Summary

This is a position paper. It runs no experiments and introduces no dataset. The authors argue that current LLM uses in English teaching mostly reuse standard NLP task framings and ignore educational methodology. They contend that LLMs can work as tutors that support human teachers rather than replace them. They organise the field around three roles. As data enhancers, LLMs create, reform and annotate learning materials and simulate students. As task predictors, they assess learners and plan learning paths. As agents, they deliver personalised and inclusive instruction. The paper relates these roles to the four core skills (listening, speaking, reading, writing). It places them in a four-stage history of AI for language education and draws on four disciplines: computer science, linguistics, education and psycholinguistics. It closes with three open challenges: hallucination, bias and privacy, and pedagogical alignment. The contribution is a conceptual map and research agenda. It matters because language is described as an ill-defined domain that has received less tutoring-system attention than maths or science.

Key points

  • The paper proposes a three-role taxonomy (data enhancers, task predictors, agents), with a literature overview in Appendix A and future directions in Appendix B.
  • It describes four generations of educational language technology:
    • rule-based (1960s–1990s; PLATO, Systran);
    • statistical (1990s–2010s; early Google Translate, Dragon NaturallySpeaking);
    • neural (2010s–2020s; Grammarly, Duolingo);
    • LLMs (2020s onward; ChatGPT, Khanmigo).
  • It cites the evaluation taxonomy of Maurya et al. (2024), which has eight dimensions for AI tutors. These fall into two groups: problem-solving ability and pedagogical alignment (adapting to proficiency, scaffolded feedback, engagement, motivation).
  • Generated questions can be answer-aware, where the target answer is fixed, or answer-agnostic, where the answer is open.
  • The authors list three limitations of their own position:
    • it emphasises potential over deployment barriers;
    • it does not analyse how the roles vary across learner groups, languages and cultures;
    • it gives only shallow treatment of how learners, tutors, teachers and parents interact.

Interesting ideas

  • I1. Separating generated exercises into ones with a fixed target answer and open-ended ones lets each type get its own checking procedure; this split is useful wherever items are produced automatically.
  • I2. An LLM can play the learner, so that materials and tutoring strategies are tried out on simulated students before real ones see them. This is a cheap way to probe a system with realistic varied inputs.
  • I3. Evaluate a tutor on two separate axes: whether it solves the task, and whether its help is pedagogically sound. Grading only correctness hides poor explanations.
  • I4. Anchor LLM output to structured domain knowledge and explicit rules, then add automatic plus human-in-the-loop validation to curb hallucinations.
  • I5. The authors frame learner language as an ill-defined domain where high variability makes automatic analysis unreliable. This is a reminder that systems tuned on clean text may break on non-native input.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (answer): Fixed-answer test items could be generated straight from the world graph, so the gold answer is known by construction. Open items could be routed to a separate, softer grading path.
  • I2 (learn): Simulated student-style texts, including learner errors, could serve as stress tests and self-play opponents for the parser and readers.
  • I3 (answer): Readers answering school tests could be scored both on answer accuracy and on whether the explanation trace is understandable and pitched at the right level.
  • I4 (learn): LLM teacher tiers could have their labels checked against compiled knowledge (VerbNet classes, WordNet ontology) before entering silver data, with disagreements sent for human review.
  • I5 (tree): Learner-written sentences could be added to evaluation as a separate slice, to measure how LAS and frame extraction degrade on non-standard English.

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

🎓Tsinghua UniversityBeijing · China415 works · 94,028 citations🎓Fudan UniversityShanghai · China202 works · 22,509 citations🎓Chinese University of Hong KongHong Kong · China159 works · 16,154 citations🎓University of Illinois ChicagoChicago · United States70 works · 20,363 citations🔬Peng Cheng LaboratoryShenzhen · China47 works · 2,699 citations🎓The Hong Kong University of Science and Technology (Guangzhou)Guangzhou · China67 works · 1,765 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksInformation Systems · 1,730 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,894 worksEnglish · 2,379 works

Cite

Cite as Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu and Qingsong Wen. 2025. Position: LLMs Can be Good Tutors in English Education. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.885

@inproceedings{ye2025position,
  title = {{Position: LLMs Can be Good Tutors in English Education}},
  author = {Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu and Qingsong Wen},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.885},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.885}
}

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