PRISM: A Personalized, Rapid, and Immersive Skill Mastery framework for personalizing experiential learning through Generative AI
Yu-Zheng Lin, Karan Patel, Ahmed Hussain J Alhamadah, Bono Po-Jen Shih, Matthew William Redondo, David Rafael Vidal Corona, Banafsheh Saber Latibari, Jesus Pacheco, Soheil Salehi, and Pratik Satam

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.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsALIGN
