A systematic review of Digital Twin (DT) and virtual learning environments (VLE) for smart manufacturing education

Abstract

The integration of Digital Twin (DT) technology with Virtual Learning Environments (VLEs) is transforming manufacturing education by enabling real-time, immersive, and adaptive learning experiences. The paper presents a systematic review of state-of-the-art applications, methodologies, and challenges in DT-VLE integration, with a focus on enhancing experiential learning and bridging the theory-to-practice gap in manufacturing education. The study systematically analyzes Simulation-based learning (SBL), Problem-based learning (PBL), Gamification based learning (GBL), and AR/VR-enhanced training, identifying their impact on student engagement, knowledge retention, and skill development in smart manufacturing education. Additionally, real-world case studies and empirical findings demonstrate that students using DT-VLE systems score up to 20% higher in problem-solving assessments and report 83% increased confidence in manufacturing concepts. Despite the evident benefits, challenges such as high implementation costs, scalability limitations, and data security concerns remain barriers to widespread adoption. To address these issues, the paper proposes a three-phase framework for DT-VLE integration, encompassing infrastructure development, adaptive course design, and industry collaborations. The findings suggest that cloud-based DT solutions, AI-powered adaptive learning pathways, and modular open-source tools can enhance scalability and accessibility, making DT-based manufacturing education more viable. The review provides a roadmap for future research and implementation, emphasizing Industry 5.0-driven smart learning environments that combine human-centric, AI-assisted, and data-driven training models. The proposed strategies aim to make digital twin education more efficient, scalable, and aligned with future workforce needs in smart manufacturing education.

Topics & Concepts