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

While the advent of the Fourth Industrial Revolution (4IR) technologies, like cloud computing, machine learning, and artificial intelligence have brought convenience and productivity improvements, they have also introduced new challenges in training and education that require the reskilling of existing employees and the building of a new workforce. Exacerbated by the already existing workforce shortages, this mammoth workforce reskilling and building effort aims to build a high-tech workforce capable of operating and maintaining these 4IR systems; requiring a higher student retention and persistence. This increase in student retention and persistence will be especially critical when training the workforce originating from marginalized communities like Underrepresented Minorities (URM), where challenges arise due to lack of access to high-quality education throughout the trainee's formative years (pre/middle/high schools), creating a cyclic set of knowledge dependencies that are difficult to meet. To address these challenges, this research presents Generative AI-based Personalized Tutor for Industrial 4.0 (gAI-PT4I4), a framework that focuses on personalization of 4IR experiential learning, using sentiment analysis to gauge student's knowledge comprehension, while using a combination of generative AI and finite automaton to personalize the content to the students' learning needs. The framework administers experiential learning, using low-fidelity Digital Twins that enable virtual reality-based (VR) training exercises focusing on 4IR training. The VR environment, integrates a generative AI teaching assistant called the Interactive Tutor, that guides the student through the training exercises, with audio and text communications.