Knowledge, attitudes, and practices toward artificial intelligence in pharmacy education and practice among pharmacy students and faculty in Bangladesh: a cross-sectional study
4 affiliations
- 1Department of Pharmacy, Faculty of Pharmaceutical Sciences, University of Science and Technology Chittagong (USTC), Bangladesh; Corresponding author.
- 2Department of Pharmacology, Bangladesh Medical University, Dhaka, Bangladesh
- 3Department of Pharmacy, Faculty of Sciences and Engineering, International Islamic University Chittagong (IIUC), Bangladesh; Department of Pharmacy, Faculty of Pharmaceutical Sciences, University of Science and Technology Chittagong (USTC), Bangladesh
- 4Department of Pharmacy, Faculty of Pharmaceutical Sciences, University of Science and Technology Chittagong (USTC), Bangladesh
Abstract
Background: Artificial intelligence (AI) is transforming healthcare systems, pharmaceutical innovation, and professional education globally. However, digital readiness for AI integration remains uneven across low- and middle-income countries (LMICs), raising concerns about equity, governance, and workforce readiness. Understanding institutional and individual readiness is crucial for sustainable execution within health systems. Objective: To assess knowledge, attitudes, and practices (KAP) regarding AI among pharmacy students and faculty in Bangladesh and identify predictors of implementation readiness. Methods: A multi-institutional cross-sectional study was conducted among 447 participants (382 students; 65 faculty) from accredited pharmacy programs following a stratified convenience sampling method. A validated KAP instrument was administered electronically. Group differences were analyzed using independent-sample t -tests, while associations and predictors were examined using Pearson correlation and multiple regression models. Results: Faculty demonstrated higher AI knowledge scores than students (4.22 ± 0.57 vs. 3.95 ± 0.73; p = 0.005; Cohen's d = 0.41). Students showed stronger support for curricular integration ( p = 0.034). Knowledge was moderately correlated with positive attitudes ( r = 0.42, p < 0.001). Regression analysis identified prior exposure (β = 0.18, p < 0.05) and workshop participation (β = 0.25, p < 0.001) as significant predictors of positive attitudes ( p < 0.01, Adj R 2 = 0.28). Despite limited institutional funding (23.1%), 72.3% of participants expressed willingness to engage in the development of national AI guidelines. Concerns regarding data privacy and regulatory oversight were consistently reported. Conclusion: Findings indicate emerging AI readiness within pharmacy education in Bangladesh, but structural capacity gaps persist. Framed through digital readiness and implementation science perspectives, the results highlight the need for coordinated curriculum reform, faculty development, and governance frameworks. Bangladesh may serve as a scalable LMIC model for ethically grounded, workforce-aligned AI integration in health systems.
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BibTeX
@article{morshed2026knowledge,
title = {{Knowledge, attitudes, and practices toward artificial intelligence in pharmacy education and practice among pharmacy students and faculty in Bangladesh: a cross-sectional study}},
author = {Md. Masud Morshed and Jannatul Ferdoush and Abu Tayab Moin and Sharmin Sultana Dina and Farjana Akter Nipa and Rukyea Yeasmin Mysha},
journal = {Dialogues in Health},
year = {2026},
month = oct,
doi = {10.1016/j.dialog.2026.100365},
url = {https://doi.org/10.1016/j.dialog.2026.100365},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Knowledge, attitudes, and practices toward artificial intelligence in pharmacy education and practice among pharmacy students and faculty in Bangladesh: a cross-sectional study
AU - Md. Masud Morshed
AU - Jannatul Ferdoush
AU - Abu Tayab Moin
AU - Sharmin Sultana Dina
AU - Farjana Akter Nipa
AU - Rukyea Yeasmin Mysha
PY - 2026
DA - 2026/10/15
JO - Dialogues in Health
DO - 10.1016/j.dialog.2026.100365
UR - https://doi.org/10.1016/j.dialog.2026.100365
AB - Background: Artificial intelligence (AI) is transforming healthcare systems, pharmaceutical innovation, and professional education globally. However, digital readiness for AI integration remains uneven across low- and middle-income countries (LMICs), raising concerns about equity, governance, and workforce readiness. Understanding institutional and individual readiness is crucial for sustainable execution within health systems. Objective: To assess knowledge, attitudes, and practices (KAP) regarding AI among pharmacy students and faculty in Bangladesh and identify predictors of implementation readiness. Methods: A multi-institutional cross-sectional study was conducted among 447 participants (382 students; 65 faculty) from accredited pharmacy programs following a stratified convenience sampling method. A validated KAP instrument was administered electronically. Group differences were analyzed using independent-sample t -tests, while associations and predictors were examined using Pearson correlation and multiple regression models. Results: Faculty demonstrated higher AI knowledge scores than students (4.22 ± 0.57 vs. 3.95 ± 0.73; p = 0.005; Cohen's d = 0.41). Students showed stronger support for curricular integration ( p = 0.034). Knowledge was moderately correlated with positive attitudes ( r = 0.42, p < 0.001). Regression analysis identified prior exposure (β = 0.18, p < 0.05) and workshop participation (β = 0.25, p < 0.001) as significant predictors of positive attitudes ( p < 0.01, Adj R 2 = 0.28). Despite limited institutional funding (23.1%), 72.3% of participants expressed willingness to engage in the development of national AI guidelines. Concerns regarding data privacy and regulatory oversight were consistently reported. Conclusion: Findings indicate emerging AI readiness within pharmacy education in Bangladesh, but structural capacity gaps persist. Framed through digital readiness and implementation science perspectives, the results highlight the need for coordinated curriculum reform, faculty development, and governance frameworks. Bangladesh may serve as a scalable LMIC model for ethically grounded, workforce-aligned AI integration in health systems.
SN - 2772-6533
ER -