Nursing Students' and Faculty Experiences with Artificial Intelligence in Education: A Qualitative Study Using the Technology Acceptance Model

Synthesis: A COREQ-guided descriptive qualitative study of AI experiences in nursing education: semi-structured interviews with 28 participants (16 nursing students, 12 faculty) across two universities, analyzed deductively through the Technology Acceptance Model (TAM) with inductive extension. Four TAM-aligned categories emerged (perceived ease of use, perceived usefulness, behavioral intention, actual use). Students used AI mainly for presentations, visual content, and clinical case analysis; faculty for course materials, academic writing, and administration. Notably, AI was also described as a source of cognitive, instructional, and psychosocial support — a relational dimension TAM does not explicitly capture. Concerns included reduced critical thinking, misinformation, plagiarism, and unequal access.

Relevance to AI in Education: This study adds qualitative, discipline-specific depth to TAM-based AI adoption research in health-professions education, and surfaces an underexplored psychosocial/emotional dimension of AI use with implications for AI Literacy, student Well-Being, and equitable access.

Key Findings

• TAM holds in nursing education. Perceived ease of use, perceived usefulness, behavioral intention, and actual use shaped AI adoption, confirming TAM's continued relevance and adding task- and role-dependent nuance from qualitative data.
• Students and faculty use AI differently. Students leaned on AI for applied tasks (presentations, visual content, clinical case analysis, care planning); faculty for productivity (course materials, literature reviews, academic writing, administration). This role-based split implies tailored rather than one-size-fits-all AI education.
• AI as psychosocial support. Several participants — mainly students — described AI as providing emotional comfort during stress, a "companion" offering reassurance and a confidential space for reflection. This extends TAM's cognitive focus and, in high-stress clinical training, positions AI's usefulness partly in emotional and relational terms.
• Concerns temper positive attitudes. Misinformation (notably inaccurate pharmacological content, raising patient-safety concerns), plagiarism, diminished critical thinking, and unequal access were voiced; faculty worried over-reliance could weaken analytical reasoning and academic integrity. Participants nonetheless continued using AI — a risk-benefit tension worth further study.
• An AI-literacy and prompting gap. Few had formal AI education; participants urged training that goes beyond technical use to include critical questioning, prompt building, and verification of AI output. Faculty also emphasized institutional governance, ethical frameworks, and balancing AI with traditional teaching.
• Nursing as a leadership field. Faculty framed nurses not just as adapting to AI but as actively shaping responsible integration in healthcare's digital transformation.

Connected Concepts

• Medical and Health Professions Education
• Technology Adoption Models
• AI Literacy
• Well-Being
• Generative AI
• Higher Education
• Workplace Learning
• Equity

Connected Articles

• AI-Powered Simulation for Nursing Education: Mixed Methods Systematic Review — AI simulation in nursing education
• When the algorithm enters the classroom: A critical integrative review of large language models, nursing education — LLMs and nursing professional identity
• Transforming Nursing Education with Artificial Intelligence: A Systematic Review (2010–2025) — Transforming nursing education with AI
• Generative AI in Scenario-Based Healthcare Education: A Systematic Review of Applications, Validation Practices, and Pedagogical Integration — GenAI in scenario-based healthcare education
• Factors Associated with Students' Adoption of Artificial Intelligence Technology in Tertiary Education: A Meta-Analytic Review — Meta-analysis of AI adoption in tertiary education

Citation

Akbaba, A., & Calik Kus, A. (2026). Nursing Students' and Faculty Experiences with Artificial Intelligence in Education: A Qualitative Study Using the Technology Acceptance Model. BMC Nursing (Article in Press).