Opportunities and Challenges of LLMs in Education: An NLP Perspective
cs.CL
Submitted: 2025-07-30
Updated: 2026-01-16
Comments: Pre-print
Journal ref: https://aclanthology.org/2026.bea-1.26/
DOI: 10.18653/v1/2026.bea-1.26
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Interest in the role of large language models (LLMs) in education is increasing, considering the new opportunities they offer for teaching, learning, and assessment.
Terminology
Abstract
Interest in the role of large language models (LLMs) in education is increasing, considering the new opportunities they offer for teaching, learning, and assessment. In this paper, we examine the impact of LLMs on educational NLP in the context of two main application scenarios: assistance and assessment, grounding them along the four dimensions -- reading, writing, speaking, and tutoring. We then present the new directions enabled by LLMs, and the key challenges to address. We envision that this holistic overview would be useful for NLP researchers and practitioners interested in exploring the role of LLMs in developing language-focused and NLP-enabled educational applications of the future.
Sources
- Exploiting the English Vocabulary Profile for L2 word-level vocabulary assessment with LLMs
- Natural Language-based Assessment of L2 Oral Proficiency using LLMs
- Rank-Then-Score: Enhancing Large Language Models for Automated Essay Scoring
- MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
- AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails
- Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction
- Qwen2-Audio Technical Report
- LLM Agents for Education: Advances and Applications
- From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning
- Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
- Evaluating Human-AI Collaboration: A Review and Methodological Framework
- Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation
- Pronunciation Assessment with Multi-modal Large Language Models
- Meta Reasoning for Large Language Models
- Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use
- Evaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities
- Multilingual Performance Biases of Large Language Models in Education
- A Survey on LLM-as-a-Judge
- LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Load
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
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