AI虛擬人賦能護理教育──開啟沉浸式教學新典範

AI Virtual Humans Empowering Nursing Education: Opening a New Paradigm for Immersive Teaching

護理教育長期仰賴標準化病人或高擬真假人進行模擬教學，然常受限於成本高昂、情境不易重現與學生難以反覆練習等問題。沉浸式虛擬實境教學結合AI虛擬人、頭戴式顯示器與自然使用者介面，可建構沉浸、互動且逼真的模擬學習環境，並具備互動參與式實作演練、個人化學習、即時回饋，以及支持自主學習與跨域學習等功能，適合導入護理課室與臨床教學。本文介紹以無程式碼平台建置AI虛擬人教案之流程，並整合AI虛擬人模擬教學模式的構成要素與應用成效。依「新世界柯克帕特里克模式」四層次檢視，現有成效證據多集中於「反應」與「學習」層次，較少評估行為改變及臨床／組織層級結果。整體而言，AI虛擬人有助於提升護病溝通技能、自信心與臨床推理／決策能力，並逐步拓展至自殺防治與社會情緒學習等領域。未來宜在周延評估平台與設備投入成本之基礎上，採用更嚴謹研究設計與多元指標，驗證其長期效益與較高層級的臨床應用成效。

Nursing education has long relied on either standardized patients or high-fidelity manikins for simulation-based teaching. However, both are constrained by high costs, limited reproducibility of real-world scenarios, and limited opportunities for repeated practice. The utilization of immersive virtual reality pedagogy, AI virtual humans, head-mounted displays, and natural user interfaces offers the potential to create learning environments that are immersive, interactive, and realistic. Related systems support participatory hands-on practice, personalized learning, real-time feedback, and both self-directed and interdisciplinary learning, making it well suited for nursing classrooms and clinical teaching. The process used to develop AI virtual human scenarios using no-code platforms is presented in this article, and the key components and educational outcomes of AI virtual human–based simulation are synthesized. When examined in the context of the four levels of the New World Kirkpatrick Model, existing evidence is concentrated primarily at the“Reaction”and“Learning”levels, with limited assessments of behavior changes or outcomes at the clinical/organizational level conducted. Overall, AI virtual humans may be used to enhance the skills and confidence of nurses in the realms of nurse–patient communication and clinical reasoning/decision-making, and their applications have been gradually expanding to other areas, including suicide prevention and social-emotional learning. In the future, based on appropriate evaluation of platform and equipment costs, more rigorous study designs and multidimensional outcome measures should be incorporated to verify long-term benefits. Also, higher-level clinical implementation effects should be taken into consideration.