Artificial intelligence (AI) has emerged as the fourth prominent industrial revolution in the era of digitalization (Kwak, Seo, & Ahn, 2022). For nursing, AI can potentially revolutionize all domains, including clinical education, care, research, and policy (Buchanan et al., 2021). Hence, academia must foster awareness and a willingness to use AI among nursing students to prepare them for their professional careers.

Understanding the potential benefits and challenges of AI can enhance nursing students' capabilities and foster their intention to integrate AI effectively into education and clinical practice. AI supports nursing students by offering language translation services, personalized feedback and suggestions, and adaptive, interactive learning (Thakur et al., 2023). AI chatbots enhance student engagement, quickly provide access to medical knowledge, and support independent learning (Saleh et al., 2025). AI in nursing education enhances critical thinking, problem-solving, and decision-making skills and enhances students' understanding of complex concepts and personal learning experiences, thereby increasing their performance (Khlaif et al., 2025). Consequently, AI chatbots enhance students' ability to apply theoretical knowledge to practical situations and passive learning (Saleh et al., 2025).

Concurrently, AI usage presents several potential negative outcomes. First, it can foster overreliance on AI-based learning at the expense of self-learning (Saleh et al., 2025), which increases the risk of academic cheating and violations of academic integrity policies (Choi et al., 2023). Second, AI may produce inaccurate or oversimplified information that may be inadequate for addressing complex or unique nursing scenarios, resulting in significant knowledge gaps (Saleh et al., 2025). Third, students who excessively rely on AI may find it challenging to develop essential new skills, such as critical thinking and effective academic writing, which are necessary for nursing competence and lifelong learning (Abdulai & Hung, 2023; Choi et al., 2023). Fourth, AI can reduce faculty–student interaction, which may negatively influence educators' ability to tailor their teaching styles according to students' needs and address the complexities of real-world clinical scenarios (Saleh et al., 2025). Despite these negative outcomes, AI technologies have not been extensively evaluated for their influence on nursing students' learning and academic achievement (Ma et al., 2025). Future research should address this gap by identifying practical ways to integrate AI into nursing education, with the aim of optimizing its benefits while mitigating its potential drawbacks.

Past research has highlighted several factors influencing AI usage intention among nursing students. A recent study revealed that successful implementation of technology in future nursing practice depends on nursing students' acceptance and usage of AI, which are influenced by intention to use AI and shaped by effort expectancy, performance expectancy, facilitating conditions, price value, social influence, hedonic motivation, and habit (Alenazi & Alhalal, 2025; Kwak, Ahn and Seo, 2022, Kwak, Seo and Ahn, 2022). From an empirical viewpoint, a study conducted in the Middle East identified a positive association between knowledge, attitude, perception, and intention and the use of AI (Al Omari et al., 2024). From a technological perspective, Kang et al. (2023) revealed that, with respect to chatbots, perceived value, perceived ease of use, perceived usefulness, and intention to use are strongly correlated; however, only perceived value influenced intention to use. Considering the variation in factors influencing usage intention, there is a strong need to assess all factors influencing intention to use AI among nursing students.

Several technological theories, including the unified theory of acceptance and use of technology (UTAUT), UTAUT2, the technology acceptance model (TAM), the theory of reasoned action (TRA), the theory of planned behavior (TPB), the diffusion of innovation (DOI) theory, and technology–organization–environment, have been applied to assess the factors that influence intention to use AI among students (Kwak, Seo, & Ahn, 2022; Labrague & Al Harrasi, 2025). Each theory offers distinct antecedents that help explain users' intention to use AI. However, the literature still lacks a comprehensive systematic review that specifically explores the technological, social, psychological, environmental, and behavioral factors influencing intention to use AI among nursing students. The identification of these factors could contribute to the existing literature by moving beyond cataloguing awareness to understanding the drivers of adoption behavior.

A limited number of systematic reviews have examined nursing students' perceptions, attitudes, and knowledge toward AI technologies, as well as the potential applications of AI in nursing education and practice (Amiri et al., 2024; Gunawan et al., 2024; O'Connor et al., 2023; Von Gerich et al., 2022). Amiri et al. (2024) performed a systematic review and meta-analysis assessing attitudes toward and knowledge of AI among nursing, medical, and dental students. Gunawan et al. (2024) reviewed the integration of the powerful AI tool ChatGPT into nursing education. O'Connor et al. (2023) synthesized the literature on the testing, usage, and assessment of AI applications in nursing and midwifery. Von Gerich et al. (2022) examined the effective application of AI-based health technologies in nursing practice. However, due to differences in target populations among the aforementioned studies and no concurrent reviews assessing willingness to use AI, the current study seeks to perform a systematic review focused on nursing students' intention to use AI in nursing education. This systematic review seeks to answer the following two research objectives:(1)

To identify the factors influencing nursing students' intention to use AI in nursing education; and

(2)

To explore the theories/models that explain the underlying phenomenon behind factors influencing nursing students' intention to use AI in nursing education.