# AI Agents and Education: Simulated Practice at Scale

arXiv: https://arxiv.org/abs/2407.12796 · HTML: https://mlsanity.com/paper/2407.12796/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2024.

## Built on by (most cited first, 5 of 5)

- [Evolution of AI in Education: Agentic Workflows](https://mlsanity.com/paper/2504.20082/) (2025)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?](https://mlsanity.com/paper/2507.08232/) (2025)
- [ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning](https://mlsanity.com/paper/2510.07456/) (2025)
- [Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence](https://mlsanity.com/paper/2605.14266/) (2026)

## Abstract (arXiv)

This paper explores the potential of generative AI in creating adaptive educational simulations. By leveraging a system of multiple AI agents, simulations can provide personalized learning experiences, offering students the opportunity to practice skills in scenarios with AI-generated mentors, role-players, and instructor-facing evaluators. We describe a prototype, PitchQuest, a venture capital pitching simulator that showcases the capabilities of AI in delivering instruction, facilitating practice, and providing tailored feedback. The paper discusses the pedagogy behind the simulation, the technology powering it, and the ethical considerations in using AI for education. While acknowledging the limitations and need for rigorous testing, we propose that generative AI can significantly lower the barriers to creating effective, engaging simulations, opening up new possibilities for experiential learning at scale.