The emergence of artificial intelligence (AI) has revolutionized numerous fields, including healthcare, by enhancing diagnostic accuracy, streamlining administrative processes, and improving patient care outcomes (Chowdhary, 2020). AI technologies, such as machine learning algorithms, natural language processing, and robotics, are increasingly being integrated into various aspects of healthcare delivery, offering unprecedented opportunities for advancing clinical practice and patient management (Alowais et al., 2023; Zahlan et al., 2023). In nursing, AI has the potential to transform practice through innovations like predictive analytics for patient monitoring, intelligent decision support systems, and personalized treatment plans, thereby improving the efficiency and effectiveness of care and patient outcomes (Seibert et al., 2021; Rong et al., 2020; Secinaro et al., 2021). Additionally, AI-powered chatbots may have the potential to assist nurses in answering clinical queries, offering real-time support and enhancing decision-making (Labrague, 2024). As AI advances and becomes more integrated into healthcare settings, its significance in nursing education is becoming increasingly clear (Buchanan et al., 2020; Rodriguez-Arrastia et al., 2022).

Integrating AI into nursing curricula is becoming increasingly crucial for equipping future nurses with the skills necessary to effectively utilize these technologies and maximize their benefits in clinical practice. Educating nursing and healthcare students about AI not only provides them with the competencies needed to operate advanced technologies but also enhances their capacity to critically assess and incorporate AI-driven insights into patient care (Baigi et al., 2023). This readiness is essential in an era where AI is anticipated to significantly influence the future of healthcare delivery (Damerji & Salimi, 2021; Kelly et al., 2023).

In recent years, the integration of AI-powered platforms in nursing education has expanded, enhancing teaching pedagogies such as AI-augmented instruction, AI-driven simulation activities, and AI-generated content (Labrague et al., 2025). While existing research indicates a rising adoption of AI-based teaching methods in nursing education (Benfatah et al., 2024; Han et al., 2022; Labrague & Al Sabei, 2024) and examines students' perspectives on AI, there remains a paucity of studies investigating the determinants of students' behavioral intentions to adopt such technology (Labrague et al., 2023a; Kwak et al., 2022a). Moreover, while no dedicated framework currently exists to guide educators in supporting the adoption and integration of AI technologies, the Technology Acceptance Model (TAM) (Davis, 1989; Davis et al., 2024) offers a robust and widely validated foundation for understanding technology acceptance. TAM's adaptability makes it particularly suitable for investigating the adoption of emerging technologies, including AI, in nursing education.

This systematic review explores nursing students' perceptions of artificial intelligence (AI) and how these perceptions influence their acceptance and integration of AI in nursing education. Understanding these perceptions will inform the development of targeted strategies to enhance AI integration into nursing curricula, ultimately fostering a more informed and prepared nursing workforce. TAM was selected as the guiding framework for this study because its constructs—Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Towards Use (ATT), and Behavioral Intention to Use (BIU)—align well with the factors that influence AI acceptance. PU refers to the degree to which a person believes that using a particular system would enhance their performance, while PEOU reflects the degree to which they believe using the system would be free of effort. ATT encompasses the overall positive or negative feelings toward using the technology, and BIU represents the individual's intention to use the technology in the future (Davis, 1989; Davis et al., 2024). By organizing the review around these TAM constructs, the study aimed to provide a comprehensive understanding of how nursing students perceive AI and the factors influencing their readiness to integrate AI into their nursing practice, while also underscoring the importance of developing a framework specifically designed for AI integration in nursing education.