An exploration of undergraduate nursing students' heavy use of generative artificial intelligence in academic practice: A descriptive phenomenological study

BACKGROUND: Generative artificial intelligence is increasingly integrated into nursing education. Within this context, heavy use of these tools has emerged among undergraduate nursing students, raising concerns about the impact on learning and competency development, yet their lived experiences underlying this phenomenon remain unexplored. OBJECTIVES: To explore the lived experiences of undergraduate nursing students who heavily use generative artificial intelligence in academic practice. DESIGN: Descriptive phenomenology interview study. SETTING: Data collection was carried out in school of nursing in Beijing, Harbin, and Jinzhou, China. PARTICIPANTS: Twenty-two undergraduate nursing students were recruited with purposive sampling. All participants were aged between 18 and 24 years. METHODS: The semi-structured interview recordings were transcribed by the research team, and transcripts were analyzed by two independent coders with Colaizzi's descriptive analysis framework. The process employed NVivo 12.0 software. RESULTS: Data analysis identified nine subthemes falling into the three macro-themes: (1) shifts in learning behavior; (2) a self-reinforcing cycle of dependency and deskilling; and (3) conflicted awareness and attempted adaptation. CONCLUSION: Heavy use of generative artificial intelligence among undergraduate nursing students manifests as shifts toward passive engagement, with students becoming trapped in a self-reinforcing cycle of dependency and deskilling even as they remain aware of risks and attempt self-regulation. While emerging guidelines focus on academic integrity and misuse prevention, further empirically validated guidance for moderate use is needed. Integrating such guidance into curricula through process-oriented assessment may enable students to harness technological benefits while safeguarding clinical competencies.