Generative artificial intelligence (AI) is a rapidly growing field with a wide range of applications (Gupta et al., 2024). AI refers to the process of developing machines, software and applications that can perform tasks typically requiring human intelligence, such as understanding natural language, analysing patterns and making decisions based on available data (Suva and Bhatia, 2024).

Thanks to its features like rich interaction possibilities and the capacity to provide accurate responses, this technology has attracted the attention of a wide range of users (Zhou and Zhang, 2024). Productive AI has the potential to increase user productivity by supporting business processes. For instance, software experts can use it to optimise coding processes. Teachers can use this technology to create effective presentations from course content. Students can interact with it to learn in detail about a particular topic. Designers can enhance their creative process by producing images and videos from given inputs. Similarly, healthcare professionals can use it as an effective tool in areas such as preparing medical documents and analysing images (Zhou and Zhang, 2024).

However, along with the benefits offered by productive AI, the overuse of these technologies also carries significant risks (Ünal and Kılınç, 2024). One such risks is AI addiction, a problem arising from the overuse of AI technologies that can lead to negative consequences with addictive tendencies (Savaş, 2024, Hu et al., 2023, Wiederhold, 2018). AI addiction is defined as over-dependence on AI technologies and applications in various aspects of life, including academic studies, daily routines and social interactions (Zhang et al., 2024). Users exhibiting AI addiction often show addictive tendencies in their interactions with AI technologies. For example, tendencies such as emotional addiction to chatbots, addiction to social chatbots and addiction to conversational AIs are prominent examples of this situation (Huang et al., 2024).

Excessive dependence on AI technologies may weaken individuals' own cognitive skills such as problem solving, creative thinking and critical analysis (Morales-García et al., 2024, Zhai et al., 2024, Bozkurt, 2023). Moreover, users' overconfidence in these systems may lead to negative consequences like not questioning the accuracy of the answers provided or ignoring ethical issues (Zhai et al., 2024, Grassini, 2023, Dempere et al., 2023 Gao et al., 2023; Xie et al., 2021). Furthermore, it is predicted that emotional attachment to chatbots may have negative effects on close relationships in real life and distract individuals from their social lives (Xie et al., 2023, Xie and Pentina, 2022). Although the relationship between AI addiction and mental health problems has not been fully determined, it is known that AI addiction may threaten people's interpersonal connections and negatively affect their mental health (Huang et al., 2024).

AI is an important innovation that continues to be effective in the field of education (Gezgin and Kurtça, 2024). When AI dependency on students is examined, it is suggested that this situation may lead to weakening of cognitive skills, decreasing motivation levels (Ahmad et al., 2023) and loss of independent thinking ability (Zhang et al., 2024) (Ahmad et al., 2023). However, students express concerns about the potential effects on creativity, critical thinking and ethical writing practices (Malik et al., 2023).

The integration of AI into nursing education can enrich the learning experiences of nursing students, enabling them to be better equipped for their professional roles in the health field (Liu et al., 2023). Especially in recent years, studies conducted on nursing students have shown that the inclusion of AI-based tools in educational processes increases learning motivation, critical thinking and problem solving skills and improves clinical decision-making skills (Labrague et al., 2025, Ma et al., 2025, Lifshits and Rosenberg, 2024). However, fast and easy access to information may lead nursing students to become more dependent on AI-based tools. This may negatively affect students' intrinsic motivation towards problem solving and their tendency to work independently (Liu et al., 2023).

In this context, over-dependence on AI may trigger anxiety and feelings of inadequacy among students when AI tools are inaccessible. This situation increases the potential for the phenomenon defined in the literature as ‘AIlessphobia - the fear of being left without AI’ to become widespread among students (Gezgin and Kurtça, 2024). This phenomenon differs from digital addiction or technology-related anxiety, referring specifically to psychological distress caused by AI tool unavailability in academic contexts (Gezgin and Kurtça, 2024). While digital addiction is generally associated with excessive and impulsive technology use and technology anxiety refers to the tension experienced during technology use (Kim et al., 2023, Dresp-Langley and Hutt, 2022) AIlessphobia describes the anxiety and stress reactions AI-dependent students cannot access these tools for academic success (Gezgin and Kurtça, 2024). While sharing similarities with technology deprivation-related fears such as nomophobia or netlessphobia, AIlessphobia specifically reflects emotional responses to AI's absence (Brand et al., 2014, King et al., 2010). Including this concept in nursing education research fills a significant gap in the literature and contributes to the identification of a new psychosocial risk domain related to AI.

This study examined the attitudes of university students studying nursing towards AI technologies, their usage patterns, their addiction levels towards AI and their thoughts about the fear of being left without AI, which is defined as ‘AIlessphobia’ and evaluated the effects of their interaction with these technologies on educational processes and clinical practices. Nursing students are a group that closely follow technological developments as an important component of health services and actively use these technologies in clinical decision-making, patient care and education processes (Wei et al., 2025, Glauberman et al., 2023). However, despite their active engagement, there is currently no nursing-specific legal regulation that governs the use of AI in clinical decision-making, which raises ethical and professional concerns (Mohammed et al., 2025). Therefore, nursing students were selected as the target group in the study; by determining their perceptions of AI, addiction levels and new fears developed in this context, important results were reached for both individual learning processes and professional competences.