EducaSim: Interactive Simulacra for CS1 Instructional Practice

2026-03-11

AI Summary

This work addresses the scalability challenge of traditional teacher training in massive online courses, which relies heavily on human experts and thus struggles to scale. To overcome this limitation, the authors propose the first simulation framework that integrates generative agents with a pedagogy-driven persona model to recreate small-section CS1 teaching scenarios. The system enables novice instructors to engage in immersive, low-cost, and high-fidelity teaching rehearsals. Built on an agent-based architecture, it incorporates diverse instructional personas and authentic course content. The framework was successfully deployed in a six-week CS1 course serving 20,000 students. Positive feedback from participating instructors demonstrates the approach’s feasibility in terms of both scalability and pedagogical effectiveness.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Machine Learning: Scalability of ML Systems

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Role play is a high-impact mode of training that has demonstrated its effectiveness in improving learning outcomes. However, it is difficult to scale to teacher instruction due to its inherent dependency on providing personnel who are both trained and available to facilitate this learning environment. This poses a challenge, especially to massive online courses which may employ and aid hundreds to thousands of novice teachers. In this work, we present EducaSim: a novel framework that uses generative agents to simulate a small-group section for teachers-in-training to practice instruction. EducaSim works by implementing diverse pedagogical-based personas, actual course material, and agent-based architectures constructed for instructional practice to provide a pedagogically rich environment for teachers-in-training to engage in role play learning -- without the costly overhead that comes with it. We share our experiences with constructing and making the tool available for experimental training and preparation in a six-week CS1 course supporting 20,000 students. We found that teachers who engaged generally saw it as a positive experience. We believe that EducaSim is an important step to providing experiential teaching practice at scale for closely-defined settings and has great potential for future applications.

Problem

• role play
• teacher training
• scalability
• CS1 instruction
• instructional practice

Innovation

• generative agents
• role play simulation
• teacher training
• pedagogical personas
• scalable instructional practice