Nursing students’ and faculty experiences with artificial intelligence in education: a qualitative study using the technology acceptance model
Year: 2026
Type: article
Abstract: The integration of artificial intelligence (AI) in healthcare and education underscores the need to understand the experiences of nursing students and faculty. Qualitative perspectives from these groups remain limited despite AI’s growing use in nursing education. To explore nursing students’ and faculty members’ experiences with AI in educational settings using the Technology Acceptance Model (TAM). A descriptive qualitative design was used, following COREQ guidelines. From December 2024 to April 2025, semi-structured interviews took place with 28 participants: 16 nursing students and 12 faculty members from two accredited universities. Data were analysed using deductive and inductive content analysis. Four TAM-aligned categories emerged: perceived ease of use, perceived usefulness, behavioural intention to use, and actual system use. Students and faculty both found AI accessible and valuable. Students used AI primarily for presentations, visual content, and case analysis, while faculty relied on it for course materials, academic writing, and administration. AI was recognized as cognitive, instructional, and psychosocial support, serving as a companion during academic challenges and stress. Participants expressed concerns about reduced critical thinking, misinformation, plagiarism, and unequal access highlighted the importance of AI literacy training and balancing AI with traditional approaches. In nursing education, AI extends beyond cognitive and instructional functions to include emotional and psychosocial dimensions. Effective integration requires structured AI literacy programs, ethical frameworks, and interdisciplinary institutional support. Not applicable.
Source: BMC Nursing
Authors: Arzu Akbaba, Afra Calik Kus
Institutions: Dokuz Eylül University, Süleyman Demirel Üniversitesi
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,090
OpenAlex ID: W7208827538
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