Large Language Models in Education: Applications, Challenges, and Future Directions

Abstract

Large language models (LLMs) like GPT-4 have quickly evolved into tools that are already being used in classrooms with students and teachers every day, for which a full-fledged rethinking of teaching, learning and assessment is required. They have provided new possibilities for personalized tutoring, automated feedback, content production, and the concern of academic integrity, factual reliability, equity, and the impact on critical thinking in the longer run. This paper provides a systematic overview of the potential applications, issues, and future prospects of using LLMs in education. Based on the latest review and empirical literature, we review the use of LLMs in personalized learning, automated assessment, discipline-specific instruction, language learning and administrative support. We then talk about the major issues surrounding their use such as cheating, bias and hallucination, dependence, privacy of data and unequal access. Last, we discuss potential avenues for future research, such as hybrid pedagogical models incorporating human and AI, domain-specific educational LLMs, multimodal adaptive learning systems, and the policy framework necessary to regulate their responsible use. Our overall assessment is that LLMs have significant teaching potential but need to be used with care and backed by evidence to prevent an impact on the ultimate purpose of education.