## 4 Discussion  
In this study, we developed a virtual patient model that integrates disease diagnosis with syndrome differentiation in Traditional Asian Medicine (TAM) education. Utilizing large language models (LLMs) and expert-guided prompt engineering, we successfully generated 28 clinically plausible virtual patient cases by combining five fatigue-associated diseases with seven TAM syndromes. The model demonstrated high proficiency in producing natural, contextually appropriate patient responses that accurately reflect both biomedical symptoms and TAM symptomatology. Through the detailed example of a patient with fibromyalgia and Spleen-Qi deficiency, we illustrated the model's ability to create realistic clinical scenarios that facilitate comprehensive history-taking and clinical reasoning practice. The prompt-component analysis further confirmed the importance of both the system prompt and the assistant-user prompt in ensuring accurate and effective sim \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Evaluation Item** & **Mean (SD)** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Validity \\ Evaluation \\ \end{tabular} } & Gen-SynDi is an appropriate tool for virtual patient history-taking and clinical reasoning. & 4.38 (0.696) \\  & Gen-SynDi includes content suitable for virtual patient history-taking and clinical reasoning. & 4.38 (0.696) \\  & Gen-SynDi adopts a format appropriate for virtual patient history-taking and clinical reasoning. & 4.31 (0.768) \\  & Total & 4.35 (0.721) \\  & Gen-SynDi is helpful for virtual patient history-taking and clinical reasoning. & 4.44 (0.704) \\  & Gen-SynDi is effective for training in virtual patient history-taking and clinical reasoning. & 4.31 (0.682) \\  & Using Gen-SynDi improves competency in virtual patient history-taking and clinical reasoning. & 4.38 (0.599) \\  & Total & 4.38 (0.665) \\  & Gen-SynDi is easy for users to use. & 4.13 (0.781) \\  & It is easy for users to find the desired features or information in Gen-SynDi. & 4.06(0.827) \\  & Gen-SynDi does not cause users to make mistakes (e.g., operational errors). & 3.75(0.901) \\  & Total & 3.98(0.854) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Expert evaluation results on the validity, educational effectiveness, and usability of Gen-SynDi. ulations. Moreover, we implemented our model in a publicly accessible format, allowing educators and practitioners to easily integrate virtual patients into their educational or clinical settings. While previous studies have approached the use of AI in TAM education to enhance clinical training, these efforts have often been constrained by specific limitations. A previous study proposed a rule-based chatbot for Korean Medicine education, providing students with practical experience and reducing instructors' workload [23]. Other researchers introduced a chatbot using LLMs for Clinical Performance Examination (CPX) practice, enabling more dynamic and flexible student interactions [24]. Additionally, we previously assessed the potential of utilizing large language models for pattern identification education by developing a virtual patient with fatigue symptoms and a dual deficiency of the heart-spleen pattern [25]. However, these studies faced limitations such as struggling with varied question phrasing due to rule-based designs, lacking implementations for evaluating clinical reasoning, or focusing on single cases. In contrast, Gen-SynDi closes these gaps on three fronts. (1) Dual-axis case design: each of the 28 scenarios couples a Western fatigue-related disease with a high-prevalence TAM syndrome, forcing learners to integrate biomedical and pattern-identification reasoning--an ability untouched by prior chatbots. (2) Robust prompt engineering: knowledge-guided templates preserve clinical consistency across student-led dialogues, overcoming the phrase-matching rigidity of rule-based systems and the drift seen in unconstrained LLM outputs.