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Predicting nurses' behavioral intention to adopt artificial intelligence in a resource-limited setting: an extended
Somaye Sohrabi1, Hossein Bonakchi2, Rahman Kazemi3
1Department of Medical Education, School of Medical Education and Learning Technologies, Shahid Beheshti University of Medical Sciences, Corner of Torj Alley, Opposite Talash Complex, Before Park-e Vey, Valiasr St. (AJ), Tehran, Iran. sohrabisomaye1@gmail.com.
BMC Nursing
|July 14, 2026
Summary
Nurses in resource-limited settings show low AI knowledge and negative attitudes, hindering adoption. Improving AI literacy and attitudes is crucial for successful healthcare integration.
Area of Science:
- Healthcare Informatics
- Artificial Intelligence in Medicine
- Technology Adoption
Background:
- Artificial Intelligence (AI) integration in healthcare promises enhanced decision-making and patient care.
- Nurse acceptance is critical for AI implementation, yet factors influencing adoption in resource-limited settings are understudied.
- The Technology Acceptance Model (TAM) is a key framework, but its application needs expansion to include cognitive and attitudinal aspects.
Purpose of the Study:
- To investigate the factors influencing nurses' acceptance and adoption of AI technologies in resource-limited healthcare environments.
- To evaluate the role of nurses' knowledge, attitudes, perceived usefulness, and perceived ease of use in AI adoption.
- To extend the TAM framework with cognitive and attitudinal variables for predicting AI implementation in nursing.
Main Methods:
- A cross-sectional analytical study was conducted in 2025 involving 199 nurses from hospitals affiliated with Ilam University of Medical Sciences, Iran.
- A validated online questionnaire assessed Perceived Usefulness, Perceived Ease of Use (TAM), knowledge, attitude, behavioral intention, and practical AI use.
- Data were analyzed using non-parametric tests and multiple linear regression (SPSS v.26).
Main Results:
- A majority of nurses exhibited low AI knowledge (70.4%) and unfavorable attitudes (66.8%).
- Behavioral intention (57.3%) and practical AI use (48.7%) were moderate to low.
- Attitude (β=0.272, p<0.001) was the strongest predictor of AI adoption, followed by perceived ease of use, perceived usefulness, and knowledge.
Conclusions:
- Extending the TAM with nurses' knowledge and attitudes effectively predicts AI adoption in resource-limited settings.
- Fostering positive attitudes and AI literacy is paramount for successful AI integration in healthcare.
- Targeted educational and organizational interventions are necessary to prepare the nursing workforce for AI-enabled healthcare.
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