臨床溝通對於促進照護協調與提升病人安全具有關鍵作用，被視為護理學生的核心臨床能力之一。儘管護理教育對此高度重視，教學現場仍充滿挑戰。其中，以客觀結構式臨床評量 (Objective Structured Clinical Examination, OSCE) 為代表的評量形式，雖能系統性檢視學生臨床表現，卻受限於場地、評估人力及標準病人等資源，使學生缺乏足夠且具深度的模擬練習機會。此外，多數學生在練習過程中難以獲得個別化且系統性的回饋，形成練習不足、回饋不明、經驗無法內化的負面循環。
為改善上述問題，本研究開發「虛擬病人輔助反思系統」 (Virtual Patient Reflection Assistance System, VPRSS) ，結合檢索增強生成 (Retrieval Augmented Generation, RAG) 技術，確保虛擬病人回應符合臨床劇本情境，並以嵌入式教學融入「新生兒護理」課程之「腸胃道入院護理評估」單元。同時引入精準教育理念，設計「診斷」與「介入」兩階段流程。「診斷」階段針對學生臨床互動進行語音辨識與語意比對，提供即時分析作為學習診斷依據。「介入」階段則運用生成式人工智慧 (Generative AI, GAI) ，依診斷結果引導學生依Gibbs六階段循環模型進行反思，促進自我調節與經驗內化。
本研究採方便取樣，選取修習該課程之護理系二年級學生，並依期中成績平均分派至實驗組與對照組。兩組皆使用虛擬病人練習，差異在於實驗組於活動後接受生成式人工智慧引導反思，對照組則進行傳統小組討論。結果顯示，VPRSS透過檢索增強生成提升虛擬病人回應與情境模擬的一致性，能在短期活動中顯著提升護理學生臨床溝通知識。而生成式人工智慧引導的反思雖未在本研究中對自我調節學習或反思能力的提升呈現顯著差異，但質性回饋揭示其對護理學生具備多重學習優勢。能幫助學生有結構地檢視臨床互動，同時提供一個低壓且可反覆操作的練習環境，幫助學生在臨床溝通常見的焦慮情境下仍能逐步建立信心與自主學習能力。

Clinical communication is essential for care coordination and patient safety, and is regarded as a core competency for nursing students. However, traditional assessments such as the Objective Structured Clinical Examination (OSCE) are constrained by limited resources, leaving students with insufficient simulation opportunities and feedback.
To address these challenges, this study developed the Virtual Patient Reflection Assistance System (VPRSS), integrating Retrieval-Augmented Generation (RAG) to ensure clinically consistent responses and embedding it into the “Gastrointestinal Admission Nursing Assessment” unit of a neonatal nursing course. The system applied a two-phase design: a “Diagnosis” phase using speech recognition and semantic matching to provide immediate analysis, and an “Intervention” phase where Generative AI guided students through Gibbs’ reflective cycle to support self-regulated learning.
Sophomore nursing students were assigned to experimental and control groups. Both practiced with the virtual patient, but only the experimental group received AI-guided reflection. Results showed that VPRSS significantly improved short-term clinical communication knowledge. Although no significant differences were found between groups in self-regulation or reflective ability, qualitative feedback revealed several learning advantages for nursing students. The AI-guided reflection helped students examine clinical interactions in a structured manner, fostered self-awareness and corrective skills, and provided a low-pressure environment for independent, repeated practice. These features enabled students to gradually build confidence and autonomy in managing communication challenges, highlighting the system’s potential value in clinical communication training.

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