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LLMs in Education: Novel Perspectives, Challenges, and Opportunities

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arxiv 2409.11917 v1 pith:NJNBLJZQ submitted 2024-09-18 cs.CL

Bashar Alhafni , Sowmya Vajjala , Stefano Bannò , Kaushal Kumar Maurya , Ekaterina Kochmar This is my paper · ORCID
classification cs.CL
keywords llmsapplicationseducationalopportunitiesareachallengeseducationrole
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The role of large language models (LLMs) in education is an increasing area of interest today, considering the new opportunities they offer for teaching, learning, and assessment. This cutting-edge tutorial provides an overview of the educational applications of NLP and the impact that the recent advances in LLMs have had on this field. We will discuss the key challenges and opportunities presented by LLMs, grounding them in the context of four major educational applications: reading, writing, and speaking skills, and intelligent tutoring systems (ITS). This COLING 2025 tutorial is designed for researchers and practitioners interested in the educational applications of NLP and the role LLMs have to play in this area. It is the first of its kind to address this timely topic.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling

    cs.LG 2026-02 reject novelty 4.0 of 10

    Concentrating synthetic preference paraphrases on low-margin pairs gives consistent but small reward-model and alignment gains in single-run experiments, while the abstract's semantic-aware, multi-benchmark claims are...

  2. Using Large Language Models to Suggest Informative Prior Distributions in Bayesian Statistics

    stat.ME 2025-06 conditional novelty 4.0 of 10

    LLMs suggested directionally correct but poorly calibrated Bayesian priors, with Claude's weak priors ranking best on KL divergence from the data.

  3. Position: LLMs Can be Good Tutors in English Education

    cs.CL 2025-02 conditional novelty 3.0 of 10

    A literature-based taxonomy casts LLMs as data enhancers, task predictors, and agents, arguing they can be effective tutors in English education without presenting new experiments.

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