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Generative artificial intelligence acceptance, anxiety, and behavioral intention in the middle east: a TAM-based
Mona Gamal Mohamed1, Polat Goktas2, Shimaa Abdelrahim Khalaf3,4
1Adult Health Nursing. RAK College of Nursing, RAK Medical and Health Sciences University, Al Qusaidat, Near RAK Hospital, PO Box: 11172, Ras Al-Khaimah, UAE. mona@rakmhsu.ac.ae.
BMC Nursing
|July 2, 2025
Summary
Generative artificial intelligence (GenAI) acceptance among Middle Eastern nursing students is influenced by performance expectancy, effort expectancy, facilitating conditions, and social influence. Reducing anxiety is crucial for fostering GenAI adoption in nursing education.
Area of Science:
- Nursing Education
- Artificial Intelligence in Healthcare
- Educational Technology
Background:
- Generative artificial intelligence (GenAI) adoption is transforming education, but its acceptance and associated anxiety among nursing students in the Middle East are understudied.
- This research extends the Technology Acceptance Model (TAM) by integrating Facilitating Conditions (FC) and Social Influence (SI) and examining the moderating effect of Anxiety on Behavioral Intention to Use (BIU) GenAI tools.
Purpose of the Study:
- To investigate the factors influencing nursing students' behavioral intention to use GenAI tools in the Middle East.
- To examine the moderating role of anxiety in the acceptance of GenAI among nursing students.
- To provide insights for culturally tailored interventions promoting AI integration in nursing education.
Main Methods:
- A cross-sectional survey was administered to 1,055 undergraduate nursing students in Egypt, Jordan, Saudi Arabia, and Yemen.
- Structural equation modeling (SEM) was used to analyze the relationships between TAM constructs (Performance Expectancy, Effort Expectancy, FC, SI), BIU, and Anxiety.
- Data were collected using validated scales for GenAI acceptance and AI anxiety, with analysis performed using SPSS and Python.
Main Results:
- The TAM constructs and Anxiety explained 75.09% of the variance in nursing students' BIU of GenAI.
- Significant positive predictors of BIU included Performance Expectancy (PE), Effort Expectancy (EE), FC, and SI.
- Anxiety demonstrated the strongest moderating effect on BIU (β=0.552, p<0.001), highlighting its critical role. Gender, year of study, and technology access also influenced acceptance and anxiety.
Conclusions:
- Reducing anxiety and strengthening support systems are essential for promoting GenAI acceptance among nursing students.
- Findings offer actionable strategies for developing culturally sensitive educational interventions to effectively integrate AI into nursing curricula.
- The study underscores the importance of addressing student anxiety to facilitate the adoption of emerging technologies in healthcare education.
Keywords:
AnxietyArtificial intelligenceBehavioral intentionNurseNursing educationStructural equation modelingTechnology acceptance modelMore Related Videos
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