Opportunities and Challenges of LLMs in Education: An NLP Perspective

2025-07-30

AI Summary

This paper addresses the core challenges and opportunities in deploying large language models (LLMs) for educational applications. Methodologically, it proposes a novel, NLP-informed four-dimensional framework—spanning reading comprehension, writing generation, spoken interaction, and personalized tutoring—that systematically integrates LLM capabilities into two pivotal educational contexts: instructional support and learning assessment. Distinct from generic surveys, this work is the first to explicitly map LLM functionalities onto structured pedagogical dimensions, delineating technical applicability boundaries and salient ethical risks. Through functional analysis and prototypical application design, it traces the evolution of language intelligence in education—from auxiliary tool to cognitive collaborator. The contribution is a theoretically grounded, empirically informed framework for developing explainable, evaluable, and intervenable language-oriented intelligent education systems, offering both conceptual clarity and actionable implementation paradigms for researchers and practitioners.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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: {em assistance} and {em 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.

Problem

• Examines LLMs' impact on education in assistance and assessment
• Explores LLMs' role in reading, writing, speaking, and tutoring
• Identifies challenges for NLP-enabled educational applications

Innovation

• LLMs assist in teaching and assessment
• Focus on reading, writing, speaking, tutoring
• Address challenges in educational NLP applications