<![CDATA[The preservation and transfer of human expertise represent persistent challenges in knowledge management, particularly when tacit knowledge—embedded within individual experience, judgment, and contextual reasoning—is difficult to document, scale, or disseminate. Although Large Language Models (LLMs) have enabled sophisticated conversational AI systems, existing implementations frequently exhibit factual inconsistencies, hallucinations, and inadequate representation of domain-specific expert reasoning. These deficiencies diminish the reliability of AI-mediated knowledge transfer in high-stakes educational, organizational, and professional contexts. This study addresses these limitations by proposing a framework for building Expert Digital Twins through Retrieval-Augmented AI Personas, providing a scalable and reliable mechanism for preserving and transferring human expertise. The proposed framework integrates six interconnected layers: knowledge acquisition from multimodal expert sources, preprocessing and semantic chunking, vector-based knowledge repository construction, retrieval-augmented generation, persona-driven interaction modeling, and multi-dimensional evaluation. Expert knowledge is systematically collected from books, interviews, speeches, academic articles, and digital media, then transformed into a structured semantic repository enabling dynamic, context-sensitive retrieval. Human-centered design principles are applied throughout to ensure authenticity, transparency, and user trust. An experimental evaluation was conducted by constructing an Expert Digital Twin from a domain expert's knowledge corpus and comparing its performance against a conventional LLM-based baseline using metrics including Faithfulness, Response Accuracy, Expert Similarity, Hallucination Rate, and User Trust. Results demonstrate that the retrieval-augmented AI persona substantially improves factual consistency, perceived authenticity, and knowledge transfer effectiveness. This study contributes a theoretically grounded and practically deployable framework that positions Expert Digital Twins as a novel paradigm for sustainable digital intelligence in knowledge-intensive domains.]]>
