A review on the use of large language models as virtual tutors

TL;DR

This review explores how large language models like GPT-3 and BERT are used as virtual tutors in education, focusing on their role in generating and evaluating educational content and assessments.

Contribution

It provides the first comprehensive overview of educational applications of LLMs, highlighting their use as virtual tutors and in student assessment.

Findings

LLMs are primarily used as virtual tutors for question generation.

GPT-3 and BERT are the most popular models in educational applications.

The field is rapidly evolving with new models continuously emerging.

Abstract

Transformer architectures contribute to managing long-term dependencies for Natural Language Processing, representing one of the most recent changes in the field. These architectures are the basis of the innovative, cutting-edge Large Language Models (LLMs) that have produced a huge buzz in several fields and industrial sectors, among the ones education stands out. Accordingly, these generative Artificial Intelligence-based solutions have directed the change in techniques and the evolution in educational methods and contents, along with network infrastructure, towards high-quality learning. Given the popularity of LLMs, this review seeks to provide a comprehensive overview of those solutions designed specifically to generate and evaluate educational materials and which involve students and teachers in their design or experimental plan. To the best of our knowledge, this is the first review of educational applications (e.g., student assessment) of LLMs. As expected, the most common role of these systems is as virtual tutors for automatic question generation. Moreover, the most popular models are GTP-3 and BERT. However, due to the continuous launch of new generative models, new works are expected to be published shortly.