The experience of using large language models for academic purposes among nursing graduate students: a descriptive phenomenological study

Large language models (LLMs) are profoundly transforming higher education. However, their deep integration into knowledge production presents unique challenges for nursing graduate students, who must balance rigorous academic training with clinical ethical responsibilities and humanistic values. To explore the authentic experiences, cognitive processes, and underlying dilemmas of nursing graduate students using LLMs for academic purposes. A descriptive phenomenological qualitative design was employed. Using purposive sampling with a maximum variation strategy, 28 nursing graduate students were recruited. Data were collected through semi-structured interviews from January to April 2026. Transcripts were analyzed using a systematic thematic analysis approach. The analysis revealed three core themes: (1) Reshaping academic cognitive patterns, highlighting a duality where participants perceived that LLMs reduced early-stage cognitive burden and research anxiety, while some also reported concerns that reliance on AI-generated content might weaken their clinical critical thinking; (2) Dilemmas of nursing ethics and academic norms, characterized by blurred patient privacy boundaries during clinical data processing and confusion regarding academic originality and researcher identity; and (3) Conflict and integration between instrumental rationality and humanistic care, revealing algorithmic limitations in conveying deep empathy and authentic patient experiences, juxtaposed with the technology’s undeniable advantage in overcoming language barriers to promote the international dissemination of local nursing concepts. Participants perceived LLMs as improving research efficiency and facilitating cross-cultural academic communication, while also expressing concerns about clinical reasoning, data privacy, academic integrity, and nursing’s humanistic values. Nursing educator should develop updated ethical guidelines for human-AI collaboration and integrate AI literacy into curricula. Not applicable.