AI acceptance and behavioral intention in pharmacy education: Scale development, psychometric validation, and correlates
Abstract
Introduction Artificial intelligence (AI) is transforming healthcare and pharmacy practice, creating an urgent need to prepare future pharmacists for AI-enabled environments. Yet validated multidimensional tools assessing AI acceptance and behavioral intention among pharmacy students remain limited. This study developed and validated the AI Acceptance and Behavioral Intention Scale (AI-ABIS) and identified acceptance correlates. Methods A cross-sectional study included pharmacy students. The AI-ABIS was developed through a Delphi process grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). Confirmatory factor analysis, measurement invariance, reliability, validity, and linear regressions were performed. Results 681 students participated. CFA supported the five-factor structure, with excellent fit (CFI = 0.981; TLI = 0.973; RMSEA = 0.051; SRMR = 0.030). The AI-ABIS showed excellent internal consistency (Cronbach's α = 0.934; McDonald's ω = 0.934), scalar invariance across academic level, AI-use confidence, and pharmacy-related AI use, and established convergent and concurrent validity. In multivariable analysis, acceptance was independently predicted by very frequent AI use (B = 6.857), use purpose diversity (B = 0.914), eHealth literacy (B = 0.241), positive AI attitude (B = 0.527), trust in AI (B = 0.286), digital health readiness (B = 0.251), and ethical consciousness (B = 0.231; all P ≤ 0.001), collectively explaining 61.5% of the variance in AI acceptance (R 2 = 0.615). Conclusion The AI-ABIS is valid and reliable for assessing AI acceptance and behavioral intention in pharmacy education. Findings support structured, applied, ethically informed AI integration to prepare future pharmacists for AI-enabled healthcare.
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BibTeX
@article{safwan2026acceptance,
title = {{AI acceptance and behavioral intention in pharmacy education: Scale development, psychometric validation, and correlates}},
author = {Jihan Safwan and Mariam Dabbous and Mona El Bakri and Reem Essa and Iqbal Fahs and Mohamad Rahal and Fouad Sakr},
journal = {Currents in Pharmacy Teaching and Learning},
year = {2026},
month = sep,
doi = {10.1016/j.cptl.2026.102802},
url = {https://hal.science/hal-05770455v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI acceptance and behavioral intention in pharmacy education: Scale development, psychometric validation, and correlates
AU - Jihan Safwan
AU - Mariam Dabbous
AU - Mona El Bakri
AU - Reem Essa
AU - Iqbal Fahs
AU - Mohamad Rahal
AU - Fouad Sakr
PY - 2026
DA - 2026/09/30
JO - Currents in Pharmacy Teaching and Learning
DO - 10.1016/j.cptl.2026.102802
UR - https://hal.science/hal-05770455v1
AB - Introduction Artificial intelligence (AI) is transforming healthcare and pharmacy practice, creating an urgent need to prepare future pharmacists for AI-enabled environments. Yet validated multidimensional tools assessing AI acceptance and behavioral intention among pharmacy students remain limited. This study developed and validated the AI Acceptance and Behavioral Intention Scale (AI-ABIS) and identified acceptance correlates. Methods A cross-sectional study included pharmacy students. The AI-ABIS was developed through a Delphi process grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). Confirmatory factor analysis, measurement invariance, reliability, validity, and linear regressions were performed. Results 681 students participated. CFA supported the five-factor structure, with excellent fit (CFI = 0.981; TLI = 0.973; RMSEA = 0.051; SRMR = 0.030). The AI-ABIS showed excellent internal consistency (Cronbach's α = 0.934; McDonald's ω = 0.934), scalar invariance across academic level, AI-use confidence, and pharmacy-related AI use, and established convergent and concurrent validity. In multivariable analysis, acceptance was independently predicted by very frequent AI use (B = 6.857), use purpose diversity (B = 0.914), eHealth literacy (B = 0.241), positive AI attitude (B = 0.527), trust in AI (B = 0.286), digital health readiness (B = 0.251), and ethical consciousness (B = 0.231; all P ≤ 0.001), collectively explaining 61.5% of the variance in AI acceptance (R 2 = 0.615). Conclusion The AI-ABIS is valid and reliable for assessing AI acceptance and behavioral intention in pharmacy education. Findings support structured, applied, ethically informed AI integration to prepare future pharmacists for AI-enabled healthcare.
SN - 1877-1297
SN - 1877-1297
ER -