A Digital Twin and Generative Artificial Intelligence Architecture for Immersive Skill Development of Maintenance Technicians in Industry 4.0

Abstract

Maintenance organizations must develop technicians faster than apprenticeship allows while experts retire and equipment grows more complex. This conceptual paper proposes the Twin-Grounded Immersive Learning Architecture, which integrates digital twins, retrieval-grounded generative artificial intelligence and immersive interfaces within a learning-science framework. It specifies what distinguishes a maintenance digital twin from a static training simulator, the fidelity and synchronization each scenario type requires, and how retrieval sources, response verification and unsafe requests are handled. Learning follows cognitive apprenticeship, with assistant support that fades from demonstration to Socratic prompting, and an adaptive rule schedules practice by failure frequency, criticality and the learner's competency gap. A simulation of 4,000 learners under an assumed learning rule shows that adaptive scheduling yields higher competency on critical faults than uniform or frequency-based scheduling, and the advantage persists when forgetting and incorrect feedback are introduced. The simulation isolates the scheduling component; it does not estimate the effect of the digital twin or the generative assistant, which require empirical pilots measuring diagnostic accuracy, unsafe actions, delayed retention and transfer to unfamiliar faults. The paper sets out an evaluation framework, controls for hallucination, over-reliance and privacy, and five propositions on transfer, retention and calibrated trust.