AI輔助教學於護理教育應用之經驗與挑戰

Experiences and challenges of AI-assisted instruction in nursing education

隨著生成式人工智慧（Generative Artificial Intelligence，GenAI）的快速發展，人工智慧（AI）已由過去需高度技術門檻的工具，轉變為可透過自然語言互動的親民科技，使非資訊背景的教師與學習者亦能有效運用。本文旨在探討AI輔助教學於護理教育中的應用模式、教學效益與挑戰，並進一步說明其在精準教育（Precision Education）理念下，如何依據不同學習者需求提供適性化學習支持與能力培育。本文除了介紹AI的發展歷程及特性，更進一步針對在護理教育中，AI扮演的角色、應用的優勢，以及實際應用範例進行解說。這幾個範例充分展示了AI在護理教育中，針對不同培訓對象發揮的潛力；包括導入GenAI於QRV（Question，Reflection，Verification）模式，提升臨床教師教學評估能力，以及透過整合GenAI與漸進式提示策略，來促進護理人員批判思考與知識建構的培訓模式。上述應用亦展現AI輔助教學可透過學習歷程分析、即時回饋與個別化引導，協助教師進行更具適性與精準性的教學設計。此外，透過語音辨識與生成式模型建構的模擬病人互動系統，使護理學生能在接近真實情境的對話中練習溝通與同理能力。另一方面，本文也針對AI導入亦帶來新的挑戰及因應之道進行闡述；例如學習者可能產生對AI工具的過度依賴、教師及學生AI素養的不足，以及倫理責任與人機協作規範尚未完整訂定。針對這些挑戰，未來護理教育應在教學設計、專業發展與制度規範上持續優化，以充分發揮AI輔助教學的潛力，並作為推動護理教育數位轉型與精準教育發展的重要基礎。

With the rapid advancement of Generative Artificial Intelligence (GenAI), artificial intelligence (AI) has evolved from a tool requiring high technical barriers to entry into an accessible technology enabling interaction through natural language. This article aims to explore the application models, teaching benefits, and challenges of AI-assisted teaching in nursing education, and further explain how it can provide personalized learning support and skills development based on the needs of different learners under the concept of precision education.This transformation empowers educators and learners without an information technology background to utilize AI effectively. This paper not only outlines the developmental trajectory and characteristics of AI, but further addresses its role and advantages with practical examples in nursing education. These exemplars fully demonstrate AI's potential across diverse training contexts in nursing education. This includes implementing GenAI within the QRV (Question, Reflection, Verification) model to enhance clinical educators' teaching assessment capabilities, and training models that integrate GenAI with progressive prompting strategies to foster critical thinking and knowledge construction among nursing staff. The above applications also demonstrate that AIassisted teaching can help teachers design more appropriate and precise lessons through learning process analysis, real-time feedback, and individualized guidance. Furthermore, a simulated patient interaction system built using speech recognition and generative models is presented; it enables nursing students to practice communication and empathy skills in near-real-world conversational settings. Conversely, this paper also addresses emerging challenges and corresponding solutions arising from AI implementation. These include potential learner over-reliance on AI tools, insufficient AI literacy among educators and students, and incomplete ethical frameworks governing human-machine collaboration. To overcome these challenges, it is suggested that future nursing education need to continuously refine instructional design, professional development, and institutional regulations to fully realize the potential of AIassisted teaching and serve as a crucial foundation for promoting the digital transformation and precision education of nursing education.