人工智慧技術，包括機器學習、深度學習、自然語言處理、生成式AI（artificial intelligence）、元宇宙等，正在迅速改變當前教育的樣貌，這些技術不僅增進教與學的過程，還提高了教育內容品質和可及性。人工智慧帶來教育變革，重塑教師教學模式，並實現學生個人化學習需求，進而改善學習成果、提高學習效率與教學實踐的效果。截至目前，AI於護理管理和臨床實務已有不少應用案例，然AI於護理教育之應用仍處萌芽階段，機器學習被用來預測護理學生的學業表現和畢業結果，從而提早識別需要額外支持的學生。自然語言處理技術則被用於開發輔助學習的聊天機器人和虛擬教師，提供個別化學習支持，幫助學生克服學習障礙。生成式AI技術，如ChatGPT（chat generative pre-trained transformer），則被用來創造模擬病人案例和作為自動學術寫作評分工具。而生成式AI技術與元宇宙的結合為護理教育帶來了新的可能性，使學生能夠在更加身臨其境的虛擬環境中學習。儘管AI技術在護理教育中帶來顯著的好處，但其實施和整合仍面臨挑戰，包括高昂的成本、需要對教師進行相應的技術培訓，以及需要解決學術誠信和數據隱私等問題。期望本文的分享能促進護理教育與資通訊專家跨域合作，推動人工智慧輔助教學，進而發展護理教育新篇章。

Artificial intelligence (AI) technologies, including machine learning, deep learning, natural language processing, generative AI, the metaverse and other iterations, are rapidly changing the landscape of education. Related technologies not only enhance the teaching and learning process but also improve the quality and availability of educational content. AI facilitates educational transformation, reshapes teaching models, and helps students achieve their personalized learning needs, thus improving learning outcomes, learning efficiencies, and teaching practices. Despite the many AI application cases in nursing management and clinical practice, the application of AI in nursing education remains in its infancy. Machine learning has been used to predict the academic performance and graduation results of nursing students, thereby facilitating the early identification of additional support needs. Natural language processing technology has been used to develop chatbots and virtual teachers to assist learning, providing personalized learning support to help students overcome learning obstacles. Also, generative AI technologies such as ChatGPT (chat generative pre-trained transformer) have been used to create simulated patient cases and as a tool for grading academic writing automatically. Moreover, the combination of generative AI technology and the metaverse has introduced new possibilities to nursing education, allowing students to learn in a more-immersive virtual environment. Despite the significant benefits brought by AI to nursing education, its implementation and integration still face multiple challenges, including high costs, the need to provide technical training to teachers, and the need to address issues such as academic integrity and data privacy. The authors hope this article will help promote interdisciplinary cooperation between nursing educators and information and communication experts and the development of AI-assisted teaching to open a new chapter in nursing education.