Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development
Journal:
arXiv
Published Date:
Feb 19, 2025
Abstract
The Fourth Industrial Revolution (4IR) technologies, such as cloud computing,
machine learning, and AI, have improved productivity but introduced challenges
in workforce training and reskilling. This is critical given existing workforce
shortages, especially in 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 Industrial 4.0, designed to personalize 4IR experiential
learning. gAI-PT4I4 employs sentiment analysis to assess student comprehension,
leveraging generative AI and finite automaton to tailor learning experiences.
The framework integrates low-fidelity Digital Twins for VR-based training,
featuring an Interactive Tutor - a generative AI assistant providing real-time
guidance via audio and text. It uses zero-shot sentiment analysis with LLMs and
prompt engineering, achieving 86\% accuracy in classifying student-teacher
interactions as positive or negative. Additionally, retrieval-augmented
generation (RAG) enables personalized learning content grounded in
domain-specific knowledge. To adapt training dynamically, finite automaton
structures exercises into states of increasing difficulty, requiring 80\%
task-performance accuracy for progression. Experimental evaluation with 22
volunteers showed improved accuracy exceeding 80\%, reducing training time.
Finally, this paper introduces a Multi-Fidelity Digital Twin model, aligning
Digital Twin complexity with Bloom's Taxonomy and Kirkpatrick's model,
providing a scalable educational framework.
