中东地区生成式人工智能的接受度、焦虑情绪与行为意向：基于技术接受模型的结构方程建模方法

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

作者信息

Mohamed Mona Gamal, Goktas Polat, Khalaf Shimaa Abdelrahim, Kucukkuya Aycan, Al-Faouri Ibrahim, Seleem Ebtisam Abd Elazeem Saber, Ibraheem Awatef, Abdelhafez Aya M, Abdullah Saleh O, Zaki Hanan Nasef, Nashwan Abdulqadir J

机构信息

Adult Health Nursing. RAK College of Nursing, RAK Medical and Health Sciences University, Al Qusaidat, Near RAK Hospital, PO Box: 11172, Ras Al-Khaimah, UAE.

UCD School of Computer Science, University College Dublin, Belfield, Dublin, Ireland.

出版信息

BMC Nurs. 2025 Jul 1;24(1):703. doi: 10.1186/s12912-025-03436-8.

DOI:10.1186/s12912-025-03436-8

PMID:40598322

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12211728/

Abstract

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.

摘要

背景

在教育中采用生成式人工智能（GenAI）正在迅速改变学习环境，但在中东地区，护理专业学生对这些技术的接受程度和焦虑情绪仍未得到充分研究。本研究通过纳入促进条件（FC）和社会影响（SI）等结构扩展了技术接受模型（TAM）。它调查了焦虑对使用生成式人工智能工具的行为意向（BIU）的调节作用。

方法

在包括埃及、约旦、沙特阿拉伯和也门在内的四个中东国家的1055名本科护理专业学生中进行了一项横断面研究。使用包含生成式人工智能接受量表和人工智能焦虑量表的结构化问卷收集数据。采用结构方程模型评估绩效期望（PE）、努力期望（EE）、FC、SI和BIU之间的关系，并将焦虑作为调节变量。使用SPSS和Python的semopy库进行描述性统计、验证性因子分析和路径分析。

结果

该模型显示出很强的解释力，TAM结构和焦虑解释了BIU中75.09%的方差。路径系数显示PE（β = 0.477，p < 0.001）、EE（β = 0.293，p < 0.001）、FC（β = 0.189，p < 0.001）和SI（β = 0.308，p < 0.001）与BIU之间存在显著正相关。焦虑具有最强的调节作用（β = 0.552，p < 0.001），表明其在塑造行为意向方面的关键作用。性别、学习年份和技术获取情况成为影响接受程度和焦虑水平的重要人口统计学变量。