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Opportunities and Challenges of LLMs in Education: An NLP Perspective

Paper · 2026 · Sowmya Vajjala, Bashar Alhafni, Stefano Banno et al.

Summary

This position-and-survey paper looks at how large language models are changing educational NLP. Earlier overviews were mostly aimed at education-technology readers or covered only narrow areas such as writing assessment. The authors instead write for NLP researchers and tie recent LLM work back to older lines of NLP research. They sort the field into two application scenarios: assistive technologies that support learners and teachers, and assessment technologies that evaluate student performance. Each scenario is examined across reading, writing, speaking and tutoring. The review mainly covers post-LLM work from NLP venues and points to earlier surveys for pre-LLM methods.

The authors find that LLMs open real opportunities, including fluent feedback, personalization, interactive tutoring and more inclusive multilingual tools. They also identify persistent problems with data availability, evaluation methodology and ethics. The paper argues for user-centred evaluation and closer collaboration between NLP researchers, educators and domain experts. It is meant as an entry point for newcomers to the area, and its appendix offers reading lists for each dimension.

Key points

  • LLMs for written GEC: few-shot prompting usually beats zero-shot, and chain-of-thought gives no clear benefit. LLMs do well on JFLEG, which rewards fluent rewrites, but lag on CoNLL-2014 and BEA-2019, which reward minimal, precise edits.
  • Spoken GEC: systems are moving from cascaded ASR → disfluency detection → GEC pipelines to end-to-end models built on Whisper. The Speak & Improve Corpus is highlighted as the first public speech dataset annotated for grammatical errors.
  • Reading tasks: for readability assessment, task-specific fine-tuned models still outperform zero- or few-shot prompting of LLMs. Text simplification is expanding toward document-level, multi-agent and personalized methods.
  • Evaluating tutor responses: current options remain weak. Human evaluation does not scale, LLM-as-judge struggles with complex pedagogical traits, and a LoRA-tuned 2B model (Naeem et al.) covers only some evaluation dimensions.
  • Ethics and scope: key concerns include transparency, privacy, fairness and hallucination, which gets worse as tasks grow more complex. Appendix Tables 1–4 list further readings for each dimension.

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 CambridgeCambridge · United Kingdom243 works · 121,396 citations🏛️National Research Council CanadaOttawa · Canada59 works · 21,677 citations🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates124 works · 5,676 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Sowmya Vajjala, Bashar Alhafni, Stefano Banno, Kaushal Kumar Maurya and Ekaterina Kochmar. 2026. Opportunities and Challenges of LLMs in Education: An NLP Perspective. Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.bea-1.26

@inproceedings{vajjala2026opportunities,
  title = {{Opportunities and Challenges of LLMs in Education: An NLP Perspective}},
  author = {Sowmya Vajjala, Bashar Alhafni, Stefano Banno, Kaushal Kumar Maurya and Ekaterina Kochmar},
  booktitle = {Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.bea-1.26},
  url = {https://doi.org/10.18653/v1/2026.bea-1.26}
}

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