Traditional engineering education struggles to bridge the theory–practice gap in the Industry 4.0 era, as static 2D schematics inadequately convey complex spatial relationships. While advanced visualization tools exist, their adoption is frequently hindered by requirements for specialized hardware and software, limiting accessibility. This study details the development and evaluation of a novel, web-based Digital Twin framework designed for accessible, intuitive e-learning that requires no client-side installation. The framework, centered on a high-fidelity 3D model of a historic radial engine, was assessed through a qualitative pilot case study with seven engineering professionals. Data was collected via a “think-aloud” protocol and a mixed-methods survey with a Likert scale and open-ended questions. Findings revealed an overwhelmingly positive reception; quantitative data showed high mean scores for usability, educational impact, and professional training potential (M > 4.2). Qualitative analysis confirmed the framework’s success in enhancing spatial understanding via features like dynamic cross-sections, improving the efficiency of accessing integrated documentation, and demonstrating high value as an onboarding tool. This work provides strong preliminary evidence that an accessible, web-based Digital Twin is a powerful and scalable solution for technical education that significantly enhances spatial comprehension and knowledge transfer.
Weis et al. (Tue,) studied this question.