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

arXiv: https://arxiv.org/abs/2405.11983 · HTML: https://mlsanity.com/paper/2405.11983/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 76 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models](https://mlsanity.com/paper/2302.04662/) (2023)
- [Repairing Bugs in Python Assignments Using Large Language Models](https://mlsanity.com/paper/2209.14876/) (2022)
- [Multiple-Choice Question Generation: Towards an Automated Assessment Framework](https://mlsanity.com/paper/2209.11830/) (2022)
- [EQG-RACE: Examination-Type Question Generation](https://mlsanity.com/paper/2012.06106/) (2020)
- [Toward Educator-focused Automated Scoring Systems for Reading and Writing](https://mlsanity.com/paper/2112.11973/) (2021)

## Built on by (most cited first, 13 of 13)

- [Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning](https://mlsanity.com/paper/2511.00222/) (2025)
- [Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval](https://mlsanity.com/paper/2503.15548/) (2025)
- [Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset](https://mlsanity.com/paper/2503.23899/) (2025)
- [Does the Prompt-based Large Language Model Recognize Students' Demographics and Introduce Bias in Essay Scoring?](https://mlsanity.com/paper/2504.21330/) (2025)
- [Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AI](https://mlsanity.com/paper/2511.06078/) (2025)
- [Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Education](https://mlsanity.com/paper/2502.20527/) (2025)
- [This Is Your Doge, If It Please You: Exploring Deception and Robustness in Mixture of LLMs](https://mlsanity.com/paper/2503.05856/) (2025)
- [How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations](https://mlsanity.com/paper/2508.05625/) (2025)
- [Evaluating the Impact of Advanced LLM Techniques on AI-Lecture Tutors for a Robotics Course](https://mlsanity.com/paper/2408.04645/) (2024)
- ["Would You Want an AI Tutor?" Understanding Stakeholder Perceptions of LLM-based Systems in the Classroom](https://mlsanity.com/paper/2503.02885/) (2025)
- [A Scoping Review of Large Language Model-Based Pedagogical Agents](https://mlsanity.com/paper/2604.12253/) (2026)
- [A funny companion: Distinct neural responses to AI- versus human-attributed humor](https://mlsanity.com/paper/2509.10847/) (2025)
- [Evaluating Epistemic Guardrails in AI Reading Assistants: A Behavioral Audit of a Minimal Prototype](https://mlsanity.com/paper/2604.27275/) (2026)

## Abstract (arXiv)

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.