PRISM: A Personalized, Rapid, and Immersive Skill Mastery framework for personalizing experiential learning through Generative AI

TL;DR

PRISM is a scalable framework that combines generative AI and Digital Twins to provide personalized, immersive, and adaptive experiential learning across various educational levels and industry training scenarios.

Contribution

This paper introduces PRISM, a novel framework integrating gen-AI and Digital Twins for personalized, rapid, and immersive skill mastery in education and training.

Findings

GPT-4 achieves 91% F1 in sentiment analysis of dialogues

GPT-3.5 performs well in informal language contexts

System demonstrates effectiveness and scalability in Industry 4.0 VR training modules

Abstract

The rise of generative AI (gen-AI) is transforming industries, particularly in education and workforce training. This chapter introduces PRISM (Personalized, Rapid, and Immersive Skill Mastery), a scalable framework leveraging gen-AI and Digital Twins (DTs) to deliver adaptive, experiential learning. PRISM integrates sentiment analysis and Retrieval-Augmented Generation (RAG) to monitor learner comprehension and dynamically adjust content to meet course objectives. We further present the Multi-Fidelity Digital Twin for Education (MFDT-E) framework, aligning DT fidelity levels with Bloom's Taxonomy and the Kirkpatrick evaluation model to support undergraduate, master's, and doctoral training. Experimental validation shows that GPT-4 achieves 91 percent F1 in zero-shot sentiment analysis of teacher-student dialogues, while GPT-3.5 performs robustly in informal language contexts. Additionally, the system's effectiveness and scalability for immersive Industry 4.0 training are demonstrated through four VR modules: Home Scene, Factory Floor Tour, Capping Station DT, and PPE Inspection Training. These results highlight the potential of integrating generative AI with digital twins to enable personalized, efficient, and scalable education.