System Design and Evaluation of RAG-Enhanced Digital Humans in Design Education: Analyzing Cognitive Load and Instructional Efficiency

Design education involves complex historical knowledge structures that often impose a high extraneous cognitive load on students. This study proposes and evaluates an intelligent instructional system that integrates Retrieval-Augmented Generation (RAG) with anthropomorphic digital humans to function as scalable cognitive scaffolding. We developed a locally deployed architecture utilizing the Qwen3-30B Large Language Model (LLM) for reasoning, BGE-Large-Zh for high-precision semantic embedding, and LiveTalking for real-time audiovisual generation. To validate the system’s pedagogical efficacy, a multi-center randomized controlled trial (RCT) was conducted across three universities (N = 150). The experimental group utilized the RAG-enhanced digital human system, while the control group received traditional instruction. Quantitative results demonstrate that the system significantly improved learning outcomes (p<0.001, Cohen’s d=1.14) and classroom engagement (p<0.001, d=1.39). Crucially, measurements using the Paas Mental Effort Rating Scale revealed a significant reduction in mental effort (p<0.001, d=1.71) for the experimental group. Instructional efficiency analysis (E) confirmed that the system successfully converted reduced extraneous load into germane learning gains (Experimental E=+0.72 vs. Control E=−0.68). These findings validate the technical feasibility and educational value of combining localized RAG architectures with embodied AI, offering a replicable framework for reducing cognitive load in intensive learning environments.