Project R-15970

Human-centered AI to enable self-regulated learning within cyber-physical labs through digital twin technology (Research)

The integration of AI and Digital Twins (DTs) in education presents a transformative opportunity to enhance personalized learning. This PhD research explores AI-driven DT frameworks to support self-regulated learning (SRL) and improve student engagement in cyber-physical labs. By leveraging real- time data, these DTs create interactive learning environments, enabling students to track progress, receive adaptive feedback, and follow customized instructions. The study addresses key challenges such as bidirectional communication between physical and virtual systems, ethical considerations about student digital twins (SDTs), and the automation of DT generation using AI. Four research objectives guide the work: (1) human-in-the-loop AI for DT co-creation, (2) privacy-aware SDT models to represent student progress, (3) synchronization of DTs with physical experiments through real-time validation and predictive modeling, and (4) AI-driven SRL mechanisms offering dynamic, personalized feedback. The research employs a modular approach, ensuring adaptability across diverse lab settings. Methodologies include iterative development, stakeholder consultations, and experimental validations in university labs. Expected outcomes include frameworks for DT-enhanced learning, AI-powered instructional feedback, and policy recommendations for ethical SDT integration. This work advances AI in education by improving SRL and accessibility to high-quality, scalable, adaptive engineering education.

01 October 2025 - 30 September 2029