Artificial intelligence has quietly moved from the margins of nursing education to its center, and a new qualitative study suggests that both students and faculty are not merely tolerating the shift but actively reshaping how they learn and teach around it. The research, published in BMC Nursing by Arzu Akbaba of Dokuz Eylul University and Afra Calik Kus of Suleyman Demirel University, applied the Technology Acceptance Model (TAM) to explore how future nurses and their educators actually experience AI tools in day-to-day academic life. The findings paint a picture of a profession in transition, one where algorithms now serve as study partners, writing assistants, and even emotional companions during periods of intense academic stress.
The study followed a descriptive qualitative design and adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ), a standardized checklist that improves the transparency and rigor of interview-based work. Between December 2024 and April 2025, the researchers conducted semi-structured interviews with 28 participants drawn from two accredited universities: 16 nursing students and 12 faculty members. Rather than testing hypotheses with numbers, the team used deductive and inductive content analysis, a dual approach that allowed themes to emerge both from the pre-established framework of the Technology Acceptance Model and from the raw, unexpected details participants volunteered in conversation.
The Technology Acceptance Model, developed originally to explain why people adopt new information systems, rests on two psychological levers: perceived ease of use and perceived usefulness. If a technology feels simple to operate and demonstrably improves performance, users form a behavioral intention to use it, which then translates into actual system use. In this study, all four TAM constructs surfaced clearly in the interview data. Both students and faculty described AI tools as accessible and valuable, meaning the classic adoption pathway was fully engaged. But the researchers went further, documenting dimensions of AI use that the original model never anticipated.
Those dimensions turned out to be strikingly different across the two groups. Nursing students reported using AI primarily for presentations, visual content creation, and case analysis, tasks that align with the visual and applied nature of clinical training. Faculty members, by contrast, leaned on AI for course material development, academic writing, and administrative work, effectively outsourcing the bureaucratic overhead that competes with teaching time. This division of labor suggests that AI is not a single monolithic tool in nursing education but a flexible instrument whose function shifts depending on where the user sits in the academic hierarchy.
Perhaps the most unexpected finding was the emotional layer. Participants described AI as providing cognitive, instructional, and psychosocial support, with some students characterizing it as a companion during academic challenges and periods of stress. In a discipline where students face high-stakes clinical placements, examinations, and the emotional weight of patient care, an always-available conversational agent appears to fill a support gap that traditional resources do not. The researchers conclude that AI in nursing education extends beyond cognitive and instructional functions into emotional and psychosocial territory, a finding that challenges purely technical models of technology adoption.
Enthusiasm, however, came with clear-eyed caution. Participants voiced concerns about reduced critical thinking, the spread of misinformation, plagiarism, and unequal access to AI tools across student populations. These worries are not trivial in nursing, a field where clinical judgment can mean the difference between life and death. If students habitually offload reasoning to a chatbot, the very skill that defines safe practice may atrophy. Misinformation carries particular danger in healthcare contexts, where plausible-sounding but incorrect answers can propagate into clinical reasoning if learners lack the foundational knowledge to catch them.
The study’s authors argue that these risks point directly toward structured AI literacy programs, ethical frameworks, and interdisciplinary institutional support. AI literacy, in this framing, is not simply knowing how to prompt a model but understanding its limitations, verifying its outputs against authoritative sources, and recognizing when its use is appropriate in an academic or clinical context. Ethical frameworks would address plagiarism and academic integrity, while institutional support would ensure that access disparities do not create a two-tier system in which some students benefit from cutting-edge tools and others are left behind.
The researchers also emphasize the importance of balancing AI with traditional pedagogical approaches. Rather than replacing lectures, textbooks, or hands-on clinical simulation, AI appears most effective when layered on top of them, accelerating routine tasks while human instructors retain responsibility for mentorship, ethical reasoning, and the nuanced judgment that defines nursing practice. This hybrid vision aligns with broader trends in health professions education, where technology is increasingly seen as an amplifier of human expertise rather than a substitute for it.
Methodologically, the study’s strength lies in its dual perspective. By interviewing both students and faculty within the same theoretical framework, the researchers captured the full ecosystem of nursing education rather than a single vantage point. The COREQ adherence and the use of both deductive and inductive analysis strengthen confidence that the four TAM-aligned categories, perceived ease of use, perceived usefulness, behavioral intention to use, and actual system use, genuinely reflect participant experience rather than researcher expectation. Ethical approval was obtained from Suleyman Demirel University’s Ethics Committee, and all participants provided written and verbal informed consent in line with the Declaration of Helsinki.
As AI tools continue to proliferate across higher education, this study offers a timely warning and a roadmap. The technology is already woven into how nursing students study and how faculty teach, and its emotional and psychosocial dimensions are real and measurable. The question is no longer whether AI belongs in nursing education but how institutions will govern it, teach it, and balance it against the human skills that remain irreplaceable at the bedside. For a profession built on trust, precision, and compassion, getting that balance right may be the most important lesson of all.
Subject of Research: Experiences of nursing students and faculty with artificial intelligence in education using the Technology Acceptance Model
Article Title: Nursing students’ and faculty experiences with artificial intelligence in education: a qualitative study using the technology acceptance model
Article References: Akbaba, A., & Kus, A. C. (2026). Nursing students’ and faculty experiences with artificial intelligence in education: a qualitative study using the technology acceptance model. BMC Nursing. https://doi.org/10.1186/s12912-026-04986-1
Image Credits: AI Generated
DOI: 10.1186/s12912-026-04986-1
Keywords: artificial intelligence, nursing education, technology acceptance model, qualitative research, AI literacy, healthcare education, Nursing, students, faculty, experiences, artificial, intelligence
News Source: Ophelia Keating. (October 16, 2026). AI Becomes a Study Partner in Nursing Schools, New Qualitative Study Finds. Scienmag.
