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AI Agents and Education: Simulated Practice at Scale

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arxiv 2407.12796 v1 pith:NHNQMUA7 submitted 2024-06-20 cs.CY

classification cs.CY
keywords practicesimulationsagentscreatingeducationgenerativelearningscale
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?

    cs.CL 2025-07 conditional novelty 5.0 of 10

    No LLM-prompt pair among 11 models and 4 prompts aligns with average NAEP student performance across math and reading in grades 4, 8, and 12.

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