Published at: Education Sciences

Generative AI-Based Platform for Deliberate Teaching Practice: A Review and a Suggested Framework

Abstract

This paper begins with a comprehensive review of the deliberate teaching practice literature related to generative AI training platforms. It then introduces a conceptual framework for a generative AI-powered system designed to simulate dynamic classroom environments, allowing teachers to engage in repeated, goal-oriented practice sessions. Leveraging recent advances in large language models (LLMs) and multiagent systems, the platform features virtual student agents configured to demonstrate varied learning styles, prior knowledge, and behavioral traits. In parallel, mentor agents—built upon the same generative AI technology—continuously provide feedback, enabling teachers to adapt their strategies in real time. By offering an accessible, controlled space for skill development, this framework addresses the challenge of scaling and personalizing teacher training. Grounded in pedagogical theory and supported by emerging AI capabilities, the proposed platform enables educators to refine teaching methods and adapt to diverse classroom contexts through iterative practice. A detailed outline of the system’s main components, including agent configuration, interaction workflows, and a deliberate practice feedback loop, sets the stage for more personalized, high-quality teacher training experiences, and contributes to the evolving field of AI-mediated learning environments.

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