Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development

• DOI: 10.48550/arxiv.2502.14080
• Publication Date: 2025-02-19
• Authors: Yuzheng Lin; Karan Petal; Ahmed Hussain J A...; Sujan Ghimire; Matthew William R...; David Rafael Vida...; Jesús Pacheco; Soheil Salehi; Pratik Satam
• Keywords: One shot; Zero (linguistics); Sentiment Analysis

The Fourth Industrial Revolution (4IR) technologies, such as cloud computing, machine learning, and AI, have improved productivity but introduced challenges in workforce training reskilling. This is critical given existing shortages, especially marginalized communities like Underrepresented Minorities (URM), who often lack access to quality education. Addressing these challenges, this research presents gAI-PT4I4, a Generative AI-based Personalized Tutor for 4.0, designed personalize 4IR experiential learning. gAI-PT4I4 employs sentiment analysis assess student comprehension, leveraging generative AI finite automaton tailor learning experiences. framework integrates low-fidelity Digital Twins VR-based training, featuring an Interactive - assistant providing real-time guidance via audio text. It uses zero-shot with LLMs prompt engineering, achieving 86\% accuracy classifying student-teacher interactions positive or negative. Additionally, retrieval-augmented generation (RAG) enables personalized content grounded domain-specific knowledge. To adapt dynamically, structures exercises into states of increasing difficulty, requiring 80\% task-performance progression. Experimental evaluation 22 volunteers showed exceeding 80\%, reducing time. Finally, paper introduces Multi-Fidelity Twin model, aligning complexity Bloom's Taxonomy Kirkpatrick's scalable educational framework.