Artificial intelligence, especially with the emergence of generative AI (gen-AI), a subset of AI that focuses on creating new content, is driving a transformative revolution across all industries. This evolution opens new avenues for training and education, creating opportunities to upskill, adapt, and modify curriculum, coursework, and training to emerging demands, keeping pace with rapid technological changes while ensuring learning efficiency, scalability, and suitability for distance learning. This book chapter proposes PRISM: Personalized, Rapid, and Immersive Skill Mastery, a scalable framework that explores the use of gen-AI, to personalize experiential learning for each trainee’s learning needs through Digital Twins and trainee sentiment analysis. This book chapter also introduces a Multi-Fidelity Digital Twin for Education (MFDT-E) framework, a scalable framework that maps Digital Twin design requirements to Bloom’s Taxonomy, and the Kirkpatrick model to access learning outcomes at different levels. The MFDT-E framework quantifies digital twins’ (DT) design requirements through their use of fidelity, classifying them broadly into three separate levels- low, medium, and high—corresponding to undergraduate, master’s, and doctoral education stages. The MFDT-E, then integrated into the PRISM framework, allows personalized experiential learning, where the trainee performs experiential learning exercises through interaction with the DT, while the training is personalized through the use of Gen-AI-based sentiment analysis to measure student knowledge comprehension instep with Bloom’s taxonomy, and usage of Retrieval-Augmented Generation (RAG) to adapt and generate new content when student comprehension drops below the targets defined in the courses learning outcomes. This study demonstrates the effectiveness of the PRISM framework by combining generative AI and multi-fidelity digital twins. GPT-4 achieved a 91% F1 score in zero-shot sentiment analysis of teacher-student dialogues, while GPT-3.5 reached 79.5% accuracy in internet slang scenarios, outperforming traditional models. A qualitative-to-quantitative sentiment approach revealed emotional dynamics during learning, supporting personalized feedback. Four VR modules–Home Scene, Factory Floor Tour, Capping Station Digital Twin, and PPE Inspection Training–were developed to deliver immersive, low-fidelity Industry 4.0 training, highlighting the system’s scalability and educational value.