## 4 Discussion
(3) Built-in analytics: our module returns both quantitative coverage scores and qualitative reasoning feedback, then triangulates those metrics with expert ratings. Together, these features turn Gen-SynDi into a full-cycle tutor that not only simulates realistic history-taking but also measures, explains, and improves student performance--bridging the gap between traditional TAM instruction and modern competency-based medical education. We also addressed the potential limitations of LLMs by combining expert knowledge with prompt engineering to prevent issues like hallucinations and unintended disclosure of correct answers (disease-syndrome combinations) [26; 27]. To mitigate the risk of hallucinations--where the LLM might generate inaccurate or nonsensical information--we anchored the model's outputs to a curated dataset based on expert knowledge [28; 29]. This ensured that the virtual patient consistently presented clinically accurate and educationally valuable information. Building on that technical safeguard, Gen-SynDi also offers a practical alternative to the ethical challenges highlighted in recent AI-in-medical-education literature [30; 31]: by replacing standardized-patient actors with reproducible LLM scripts, it avoids privacy-and-consent concerns, respects emerging neurorights principles, and provides an audit trail for instructors. Although it does not eliminate all ethical questions, this expert-guided, transparent design supplies a credible complementary solution and delivers 28 diverse disease-pattern scenarios at a fraction of the logistical cost of recruiting actors and multiple experts, making it a scalable and resource-efficient tutor for TAM clinical training. The expert review offers a nuanced picture of Gen-SynDi's current strengths and weaknesses. Validity (M = 4.35, 95 % CI 4.03-4.67) and educational effectiveness (M = 4.38, 95 % CI 4.09-4.67) were both rated \"high\", supporting our claim that the 28 scenarios are clinically plausible and pedagogically valuable. By contrast, overall usability was only \"moderate\" (M = 3.98, 95 % CI 3.64-4.32), with free-text feedback pointing to confusing navigation labels and the absence of guardrails against accidental input errors. These findings align with prior reports that novice users find LLM-based simulators more cognitively demanding than rule-based interfaces. Gen-SynDi has several potential limitations that frame our next research steps. Methodological transparency awaits full confirmation through independent replications; likewise,our expert panel evaluation offers only short-term evidence, with no head-to-head trials against conventional CPX or rule-based chatbots. The current case library is confined to fatigue presentations and text-only history-taking, so key differentiators such as laboratory or immunologic markers--crucial for teasing apart fibromyalgiadia from ME/CFS-- remain outside the simulation, and cross-cultural validity has yet to be tested beyond Korean TAM nosology. Finally, while safety prompts and audit logs curb hallucinations and privacy risks, broader questions about cognitive autonomy and over-reliance on AI still warrant scrutiny. To address these limitations, a human-in-the-loop design strategy could be adopted, whereby instructors review and adjust AI-generated evaluation feedback before it is provided to learners. Alternatively, a participatory design approach could be employed, involving both instructors and learners in the development process to ensure that the AI model is co-designed in a way that preserves learners' cognitive autonomy. Even with these constraints, Gen-SynDi is, to our knowledge, the first openly accessible tutor that pairs disease diagnosis with TAM pattern identification and automatically provides qualitative and quantitative feedback, both questioning coverage and reasoning quality, providing an affordable bridge between phenotype-based teaching and competency-based assessment.