Nursing Students Using Generative AI for Theses Report Benefits and Concerns

All nineteen undergraduate nursing students interviewed in a recent qualitative study reported using generative artificial intelligence while completing their graduation theses. Researchers identified thirty-eight distinct themes distributed across three primary dimensions: student emotions, operational pain points, and cognitive and evaluative aspects of AI-assisted academic writing.

The investigation was conducted through semi-structured interviews carried out between April 30 and May 31, 2026. The researchers selected nineteen undergraduate nursing students from a medical college and its affiliated hospital in China using a convenience sampling approach, with ethical review approval granted by the hospital.

To analyze the interview transcripts, the research team implemented Colaizzi’s seven-step analysis method. These qualitative analytical steps were further supplemented by consensus meeting discussions among the investigators to process and categorize the collected data.

Completing an undergraduate graduation thesis is a sequential process encompassing topic selection, proposal formulation, data collection and analysis, and final manuscript writing. Nursing students commonly face obstacles during these phases, including limited academic writing experience, insufficient skills in literature retrieval and critical evaluation, and weak statistical analysis capabilities, which are further compounded by supervisors being unable to provide continuous personalized guidance throughout the entire process.

Participants reported that generative artificial intelligence was widely utilized and offered meaningful assistance by improving overall efficiency, organizing structural frameworks, and supporting academic writing. Concurrently, the students documented experiencing periodic emotional fluctuations and multiple procedural pain points across the sequential stages of their thesis work.

Alongside the perceived advantages, students raised significant concerns regarding generative artificial intelligence, specifically pointing to language stiffness, the generation of false literature, and the risk of academic misconduct. Based on these qualitative findings, the authors concluded that nursing educators should establish clear usage guidelines to regulate student behavior while offering phased methodological and psychological support throughout the thesis process.

The rationale for the investigation centers on an acknowledged gap in medical education research, where qualitative evidence examining how nursing undergraduates experience and employ generative artificial intelligence across every phase of thesis completion has remained scarce. While the technology presents novel possibilities for academic support, nursing students must navigate a demanding sequential process spanning initial topic selection, proposal formulation, data collection and analysis, and final thesis writing. These baseline challenges are further compounded by instructional limitations, as academic supervisors cannot provide continuous personalized guidance throughout the entire research process.

In addressing these educational conditions, the qualitative research design gathered direct accounts from nineteen undergraduate nursing students who were recruited from a medical college in China and its affiliated hospital through convenience sampling. Conducted between April 30 and May 31, 2026, after securing ethical review approval from the hospital, the semi-structured interviews focused specifically on capturing student perspectives throughout the thesis experience. The investigators evaluated the interview transcripts using Colaizzi’s seven-step analysis method, pairing the qualitative framework with consensus meeting discussions among the research team to systematically categorize the data. This analytical approach yielded thirty-eight distinct themes categorized under three overarching dimensions: student emotions, operational pain points, and cognitive and evaluative aspects of AI-assisted writing.

The resulting thematic structure illuminates the dual nature of student engagement with artificial intelligence, as all nineteen participants confirmed utilizing generative tools while simultaneously managing recurring emotional fluctuations and practical hurdles. Although respondents considered the technology genuinely advantageous for improving task efficiency, structuring conceptual frameworks, and facilitating academic writing, these benefits were offset by serious procedural and ethical anxieties. In particular, students expressed explicit concern over the generation of false literature, stylistic language stiffness, and potential academic misconduct. To mitigate these risks, the study emphasizes that nursing educators must actively regulate student behavior by establishing explicit usage guidelines, while concurrently delivering phased methodological guidance and psychological support tailored to the sequential milestones of the graduation thesis.

Subject of Research: Generative artificial intelligence use in undergraduate nursing thesis writing

Article Title: Undergraduate nursing students’ experiences of conducting graduation theses and using generative artificial intelligence: a qualitative study

Article References: Yin, Y., Hu, Y., Wang, T., Long, J., & Li, L. (2026). Undergraduate nursing students’ experiences of conducting graduation theses and using generative artificial intelligence: a qualitative study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10698-6

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10698-6

Keywords: nursing education, generative artificial intelligence, undergraduate theses, qualitative research, academic writing, academic misconduct, medical education, Colaizzi method, Undergraduate, nursing, students, experiences