Cyber–physical systems have become critical across industries. They have driven investments in education services to develop well-trained engineers. Education services for cyber–physical systems require the hiring of expert tutors with multidisciplinary knowledge, as well as acquiring expensive facilities/equipment. In response to the challenges posed by the need for the equipment and facilities, a metaverse-based education service that incorporates digital twins has been explored as a solution. However, the issue of recruiting expert tutors who can enhance students’ achievements remains unresolved, making it difficult to effectively cultivate talent. This paper proposes a reference architecture for a learner-centric educational metaverse with an intelligent tutoring framework as its core feature to address these issues. We develop a novel explainable artificial intelligence scheme for multi-class object detection models to assess learners’ achievements within the intelligent tutoring framework. Additionally, a genetic algorithm-based improvement search method is applied to the framework to derive personalized feedback. The proposed metaverse architecture and framework are evaluated through a case study on drone education. The experimental results show that the explainable AI scheme demonstrates an approximately 30% improvement in the explanation accuracy compared to existing methods. The survey results indicate that over 70% of learners significantly improved their skills based on the provided feedback.

High-Mix Low-Volume (HMLV) manufacturing increasingly relies on heterogeneous robot fleets, but automatic generation of vendor-specific robot control code remains difficult due to platform fragmentation and safety-critical feasibility constraints. Although recent Large Language Model (LLM)-based approaches have shown promise for translating natural language into robot programs, they remain largely limited to single-platform or simulation-oriented settings and are vulnerable to physical hallucination, including spatially inconsistent commands and dynamically infeasible motions. This paper proposes a Digital Twin-integrated verification framework for adaptive control code generation in heterogeneous robot systems. The framework uses a structured intermediate task representation to support runtime spatial grounding, robot selection, pre-execution dynamics validation, and adaptive motion scaling before vendor-specific code generation and execution. Evaluation on 170 task-description scenarios and eight robot selection tasks showed improved ranking discriminability in lightweight stress cases where conventional baselines exhibited limited separation. In addition, adaptive dynamics scaling enabled safe execution in all analytically verified test cases, compared with 50% without scaling. These results suggest that Digital Twin-grounded verification and adaptive feasibility control can improve the reliability of LLM-based multi-vendor robot programming and help mitigate physical hallucination in heterogeneous robot systems.