Generative artificial intelligence acceptance, anxiety, and behavioral intention in the middle east: a TAM-based structural equation modelling approach

Background Adopting generative artificial intelligence (GenAI) in education rapidly transforms learning environments, yet nursing students’ acceptance and anxiety toward these technologies remain underexplored in Middle Eastern contexts. This study extends the Technology Acceptance Model (TAM) by in...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:BMC nursing Jg. 24; H. 1; S. 703 - 16
Hauptverfasser: Mohamed, Mona Gamal, Goktas, Polat, Khalaf, Shimaa Abdelrahim, Kucukkaya, Aycan, Al-Faouri, Ibrahim, Seleem, Ebtisam Abd Elazeem Saber, Ibraheem, Awatef, Abdelhafez, Aya M., Abdullah, Saleh O., Zaki, Hanan Nasef, Nashwan, Abdulqadir J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 01.07.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Accreditation
Anxiety
Artificial intelligence
Artificial intelligence and nursing practice
Behavioral intention
Digital infrastructure
Digital literacy
Ethics
Generative artificial intelligence
Human acts
Human behavior
Influence
Medicine
Medicine & Public Health
Nurse
Nursing
Nursing education
Nursing Management
Nursing Research
Psychological aspects
Students
Surveys
Technology Acceptance Model
Technology adoption
Variables
Middle East
Saudi Arabia
Egypt
Yemen
Nursing education
Technology acceptance model
Behavioral intention
Structural equation modeling
Anxiety
Artificial intelligence
Nurse
ISSN:1472-6955, 1472-6955
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Background Adopting generative artificial intelligence (GenAI) in education rapidly transforms learning environments, yet nursing students’ acceptance and anxiety toward these technologies remain underexplored in Middle Eastern contexts. This study extends the Technology Acceptance Model (TAM) by incorporating constructs such as Facilitating Conditions (FC) and Social Influence (SI). It investigates the moderating role of Anxiety on Behavioral Intention to Use (BIU) generative AI tools. Methods A cross-sectional study was conducted among 1,055 undergraduate nursing students across four Middle Eastern countries, including Egypt, Jordan, Saudi Arabia, and Yemen. Data were collected using a structured questionnaire comprising the Generative Artificial Intelligence Acceptance Scale and the Artificial Intelligence Anxiety Scale. Structural equation modeling was employed to evaluate relationships among Performance Expectancy (PE), Effort Expectancy (EE), FC, SI, and BIU, with Anxiety as a moderator. Descriptive statistics, confirmatory factor analysis, and path analysis were performed using SPSS and Python’s semopy library. Results The model demonstrated strong explanatory power, with 75.09% of the variance in BIU explained by the TAM constructs and Anxiety. Path coefficients revealed significant positive relationships between PE (β = 0.477, p  < 0.001), EE (β = 0.293, p  < 0.001), FC (β = 0.189, p  < 0.001), and SI (β = 0.308, p  < 0.001) and BIU. Anxiety had the strongest moderating effect (β = 0.552, p  < 0.001), indicating its critical role in shaping behavioral intentions. Gender, year of study, and access to technology emerged as significant demographic variables influencing acceptance and anxiety levels. Conclusions This study emphasizes the importance of reducing anxiety and enhancing support systems to foster GenAI acceptance among nursing students. The findings provide actionable insights for designing culturally tailored educational interventions to promote the effective integration of AI in nursing education. Clinical trial number Not applicable.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1472-6955
1472-6955
DOI:10.1186/s12912-025-03436-8