The study used a grounded theory approach, following the methodology of Strauss and Corbin, to build an explanatory model from the ground up rather than testing pre-existing hypotheses. Between August and October 2025, the researchers conducted semi-structured, in-depth interviews with 23 master’s nursing students drawn from multiple provinces in China. Participants were recruited through purposive and theoretical sampling, meaning the team deliberately selected interviewees who could illuminate the emerging theory and continued adding interviews until the model stopped generating new insights. The first 21 interviews fed directly into theory development, while two additional interviews were used to assess theoretical saturation. No new categories or shifts in the relationships between categories appeared in those final two interviews, giving the researchers confidence that the model had stabilized.

Analysis followed the classic three-stage coding sequence of grounded theory. The interview transcripts were first broken down through open coding, in which discrete concepts and events were labeled and compared. Axial coding then reassembled those fragments by identifying relationships among categories, linking conditions, actions, and consequences. Finally, selective coding distilled the material around a central phenomenon that organizes the entire model. The result is a process model describing how generative artificial intelligence becomes embedded in postgraduate learning and research routines, and the picture it paints is more nuanced than simple headlines about dependency might suggest.

At the heart of the model is what the researchers call a forward shift in the problem-solving pathway. Traditionally, a graduate student facing a research problem might first attempt to reason through it, consult textbooks or journals, seek advice from a supervisor, and only then, perhaps, try an AI tool. The study suggests this sequence is being inverted. Students now begin with the chatbot, asking it to summarize literature, draft text, interpret data, or generate ideas before they have engaged deeply with the problem themselves. Generative artificial intelligence, in other words, is migrating from the end of the pipeline to its front door, and that migration changes not just efficiency but the very structure of how students think.

What drives this shift? The researchers identified four contextual conditions that push students toward AI-first behavior. The first is impeded task progress: when a problem stalls, the temptation to break the deadlock with a machine-generated answer grows. The second is time pressure, a constant in clinical and academic life alike, which makes the slow work of independent inquiry feel like a luxury. The third is insufficient support, meaning that when supervisors, peers, or institutional resources fall short, the chatbot fills the vacuum. The fourth is ambiguous norms, the absence of clear institutional guidance about when and how AI use is acceptable. Together, these conditions create an environment in which turning to AI first is not a moral failing of individual students but a rational adaptation to structural circumstances.

Once initiated, the pattern becomes self-consolidating through what the authors describe as pathway reconstruction. This involves two intertwined behaviors: artificial intelligence-first use, in which the tool becomes the default entry point for new tasks, and cognitive task outsourcing, in which components of thinking, such as synthesizing evidence, structuring arguments, or refining language, are delegated to the machine. Each outsourcing decision reduces the friction of the next one, and over time the pathway itself is rebuilt around the technology. The student who once struggled through a literature review alone now cannot remember how they ever managed without one.

Crucially, the model identifies two reinforcement mechanisms that stabilize this pattern: Functional Benefits and Emotional Benefits. Functional benefits are the practical payoffs, including saved time, improved output quality, and smoother completion of demanding assignments. Emotional benefits are subtler but no less powerful, encompassing reduced anxiety, a sense of companionship with an always-available assistant, and relief from the isolation that often accompanies postgraduate research. Because both types of reward arrive immediately and reliably, while the potential costs, such as erosion of independent analytical skill, are delayed and diffuse, the behavior is reinforced on an uneven playing field. The brain’s learning machinery, responsive to immediate reward, tilts toward repetition.

The study does not portray students as oblivious to these risks. On the contrary, participants developed awareness that something might be lost when thinking is outsourced, worrying about accuracy, originality, academic integrity, and their own long-term competence. Yet the researchers found that this awareness rarely translated into stopping. Instead, students maintained the pattern through two mechanisms: delayed regulation, in which plans to cut back or verify AI output were perpetually postponed, and stage-based rationalization, in which use was justified according to the phase of a project, for example by telling oneself that AI assistance was acceptable for brainstorming but not for final writing, a boundary that proved porous in practice. The result is a form of negotiated coexistence, an uneasy but durable accommodation between the student’s critical judgment and the tool’s constant availability, rather than a clean decision to discontinue.

The implications for health professions education reach well beyond nursing. Graduate students in every discipline face the same four conditions: stalls, deadlines, thin support networks, and unclear rules. If the forward shift observed here is a general phenomenon, then the moment of educational intervention matters enormously. The authors argue that responses should move upstream, shaping when and how generative artificial intelligence is used rather than merely policing what students submit at the end. Practically, that means strengthening research competence so students have the skills to engage problems independently, reinforcing human support systems so that the chatbot does not become the only responsive mentor in a student’s life, and embedding artificial intelligence literacy alongside critical verification training so that students learn to interrogate machine output rather than absorb it.

The study also carries a warning embedded in its optimism. A negotiated coexistence is not a stable equilibrium; it is a truce that can tip either way depending on the environment. Institutions that provide clear norms, timely feedback, and robust mentorship could tip it toward reflective, bounded use, in which AI amplifies rather than replaces thinking. Institutions that remain silent could tip it toward habitual outsourcing, producing clinicians who can navigate a chatbot fluently but struggle to reason through an unfamiliar clinical problem without one. The grounded theory model built from these 23 interviews offers educators a map of the terrain: the conditions that trigger AI-first behavior, the pathway that reconstructs itself around the tool, and the reinforcements that lock the pattern in place. Reading that map early, the authors suggest, may be the best chance education has to shape a technology that is already reshaping its students. The research was funded by the Zhejiang Provincial Health Industry Science and Technology Plan and approved by the Ethics Committee of Sir Run Run Shaw Hospital, and it is published open access for the wider education and policy community to build upon.

Subject of Research: Generative AI-first problem solving behavior among master's nursing students

Article Title: When GenAI becomes the default: a grounded theory study of GenAI-first problem solving among master’s nursing students

Article References: When GenAI becomes the default: a grounded theory study of GenAI-first problem solving among master’s nursing students. (n.d.). https://doi.org/10.1186/s12909-026-10427-z

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10427-z

Keywords: generative artificial intelligence, nursing education, grounded theory, postgraduate students, AI dependency, problem solving, medical education, qualitative research, large language models, cognitive outsourcing, AI literacy, China