AI acceptance and behavioral intention in pharmacy education: Scale development, psychometric validation, and correlates.

Authors

Jihan Safwan, Mariam Dabbous, Mona El Bakri, Reem Essa, Iqbal Fahs, Mohamad Rahal, Fouad Sakr

Published in

Currents in pharmacy teaching & learning. Volume 18. Issue 12. Pages 102802. Sep 29, 2026. Epub Sep 29, 2026.

Abstract

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.
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.
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 (R² = 0.615).
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.

PMID:
42810188
Bibliographic data and abstract were imported from PubMed on 30 Sep 2026.

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