# Challenges and Opportunities of Moderating Usage of Large Language Models in Education

arXiv: https://arxiv.org/abs/2312.14969 · HTML: https://mlsanity.com/paper/2312.14969/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2023.

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues](https://mlsanity.com/paper/2503.06424/) (2025)
- [One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations](https://mlsanity.com/paper/2405.05581/) (2024)
- [Interactive Sketchpad: A Multimodal Tutoring System for Collaborative, Visual Problem-Solving](https://mlsanity.com/paper/2503.16434/) (2025)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators](https://mlsanity.com/paper/2604.01114/) (2026)
- [Talk2X -- An Open-Source Toolkit Facilitating Deployment of LLM-Powered Chatbots on the Web](https://mlsanity.com/paper/2504.03343/) (2025)
- [Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation](https://mlsanity.com/paper/2606.01020/) (2026)

## Abstract (arXiv)

The increased presence of large language models (LLMs) in educational settings has ignited debates concerning negative repercussions, including overreliance and inadequate task reflection. Our work advocates moderated usage of such models, designed in a way that supports students and encourages critical thinking. We developed two moderated interaction methods with ChatGPT: hint-based assistance and presenting multiple answer choices. In a study with students (N=40) answering physics questions, we compared the effects of our moderated models against two baseline settings: unmoderated ChatGPT access and internet searches. We analyzed the interaction strategies and found that the moderated versions exhibited less unreflected usage (e.g., copy & paste) compared to the unmoderated condition. However, neither ChatGPT-supported condition could match the ratio of reflected usage present in internet searches. Our research highlights the potential benefits of moderating language models, showing a research direction toward designing effective AI-supported educational strategies.