A large language model-driven virtual simulation platform advances intelligent construction teaching within an industry-education fusion framework

Growing demand for composite talent in intelligent construction has laid bare persistent gaps between traditional simulation-based teaching and the integrative competencies industry now expects. This paper presents the design, implementation, and empirical evaluation of a large language model (LLM)-driven virtual simulation teaching platform operating within an industry–education fusion framework. The platform adopts a four-layer microservice architecture integrating a domain-adapted LLM inference engine with retrieval-augmented generation and low-rank adaptation fine-tuning, a building information modeling (BIM)-driven three-dimensional scene assembler, a real-time physics simulation backend, and Bayesian knowledge tracing for adaptive difficulty calibration. Seven simulation modules covering representative intelligent construction workflows were developed and populated with authentic enterprise project data. A controlled experiment with 128 undergraduate participants over eight weeks compared the proposed platform against a conventional virtual simulation system. Results showed that the LLM-driven platform produced large effect sizes across knowledge mastery (Cohen’s d = 1.08), practical skill proficiency ( d = 1.04), and integrative competency ( d = 0.99), with the most pronounced advantages in open-ended tasks requiring cross-trade coordination and professional judgment. An analytic hierarchy process combined with fuzzy comprehensive evaluation (AHP-FCE) registered a 21.6% improvement in overall industry–education fusion effectiveness. Lower-performing students benefited disproportionately, with gains 58.2% larger than those of higher-performing peers. Taken together, these results indicate that embedding LLMs into virtual simulation environments can meaningfully advance adaptive engineering education; single-institution sampling, an eight-week horizon, and a residual hallucination risk temper the conclusions and mark clear priorities for follow-up work.