Pharmacy students' perceptions of artificial intelligence integration in pharmacy practice: Ethical challenges in multiple countries of the MENA region.

Pharmacy Students’ Perceptions of Artificial Intelligence Integration in Pharmacy Practice: Ethical Challenges in Multiple Countries of The MENA Region - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Curr Pharm Teach Learn . Author manuscript; available in PMC: 2026 Jun 5. Published in final edited form as: Curr Pharm Teach Learn. 2025 Jun 5;17(9):102397. doi: 10.1016/j.cptl.2025.102397 Pharmacy Students’ Perceptions of Artificial Intelligence Integration in Pharmacy Practice: Ethical Challenges in Multiple Countries of The MENA Region Hisham E Hasan a, * , Deema Jaber b , Omar F Khabour c , Karem H Alzoubi d, e, * Author information Article notes Copyright and License information a Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid 22110, Jordan b Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa 13110, Jordan c Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan d Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates e Faculty of Pharmacy, Jordan University of Science and Technology, Irbid 22110, Jordan Authors’ contributions Conceptualization, HEH; methodology, HEH, DJ, OFK and KHA; software, HEH; validation, HEH, DJ, OFK and KHA; formal analysis, HEH and DJ; investigation, HEH, DJ, OFK and KHA; resources, HEH; data curation, HEH, DJ, OFK, and KHA; writing—original draft preparation, HEH; writing—review and editing, HEH, DJ, OFK, and KHA; visualization, HEH; supervision, HEH, DJ, OFK and KHA; project administration, HEH, DJ, OFK and KHA; funding acquisition, HEH, OFK, and KHA. All authors have read and agreed to the published version of the manuscript. * Corresponding author hehassan23@ph.just.edu.jo (HEH), khalzoubi@just.edu.jo (KHA), Faculty of Pharmacy, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan. fax: +96227095123. Issue date 2025 Sep. PMC Copyright notice PMCID: PMC12213116  NIHMSID: NIHMS2088297  PMID: 40480187 The publisher's version of this article is available at Curr Pharm Teach Learn Abstract Background: The integration of artificial intelligence (AI) into pharmacy practice has the potential to advance learning experiences and prepare future pharmacists for evolving healthcare needs. However, it also raises ethical considerations that need to be addressed carefully. This study aimed to explore pharmacy students’ attitudes regarding AI integration into their future pharmacy practice. Methods: A cross-sectional design was employed, utilizing a validated online questionnaire administered to pharmacy students from diverse demographic backgrounds in multiple countries of the Middle East and North Africa (MENA) region from August 2022 to January 2023. Demographic, education, and work information data were, respectively, collected from study participants. In addition, technology literacy and AI familiarity were collected using a Likert scale on skill and a Likert scale on familiarity. Finally, participants’ concerns and perceived barriers regarding AI integration were collected based on a Likert scale on agreement. Results: A total of 702 pharmacy students participated in the study, with the majority being female (72.8%), enrolled in public universities (55.6%), and not employed (64.2%). Participants expressed a generally negative attitude toward AI integration, where 56.2–70.8% of respondents agreed/strongly agreed to concerns/barriers including patient data privacy (62.0%), susceptibility to hacking (56.2%), potential job displacement (69.3%), cost limitations (66.8%), access (69.1%), the absence of regulations (68.1%), and training (70.4%), physicians’ reluctance (65.1%), and patient apprehension (70.8%). Factors including country of residence, academic year, cumulative GPA, work status, technology literacy, and AI understanding influenced participants’ attitudes. Positive correlations were found between attitude score and tech-savviness (r = 0.174), and AI understanding (r = 0.155). Conclusion: Pharmacy students from multiple countries in the MENA region express significant ethical and practical concerns about AI’s integration into their future practice. These findings underscore the need for incorporating AI education within pharmacy curricula, alongside the development of robust ethical guidelines and regulatory policies. Addressing students’ concerns is crucial to ensuring ethical, equitable, and beneficial AI integration in future pharmacy practice. Keywords: Artificial intelligence, pharmacy education, pharmacy practice, ethics, technology literacy, MENA Introduction The landscape of healthcare is undergoing a profound transformation, accelerated by the widespread adoption of digital tools and artificial intelligence (AI) based services. 1 , 2 As society becomes increasingly digitized, the integration of AI in healthcare practice has emerged as a significant trend. 1 , 2 For example, the integration of AI in healthcare is revolutionizing pharmacy practice by enhancing precision in patient care, optimizing treatment regimens, and enabling personalized medicine. 3 However, there is limited research on pharmacy students’ perceptions of AI and its integration into their future professional roles. 4 – 6 This represents a critical gap as pharmacy education adapts to emerging technological advancements. As AI-driven tools, such as ChatGPT, are increasingly integrated into practice training settings, they offer opportunities for enhancing practical pharmacy training courses through accessible, engaging, personalized, real-time learning experiences and data-driven insights. 7 However, challenges related to AI’s contextual understanding, reliance on external data, potential bias, and ethical issues, such as plagiarism and overdependence, must also be considered. 8 , 9 Global professional pharmacy organizations’ recommendations, like those by the International Pharmaceutical Federation, emphasize the importance of incorporating digital health and AI instruction into pharmacy curricula. 10 While students generally show positive perceptions of AI in healthcare, concerns about privacy breaches, misinformation, and the potential disruption of traditional healthcare education remain as vulnerabilities that must be addressed. 5 , 11 Future pharmacists must integrate AI ethically into patient care, collaborate with AI-driven tools, and contribute to healthcare system enhancements. Given these complexities, this study aims to explore pharmacy students’ attitudes regarding AI integration into their future pharmacy practice. Materials and Methods Study Design This study employed a cross-sectional design. A validated self-administered online questionnaire, using Google Forms, served as the primary tool for data collection, which took place from August 2022 to January 2023. Questionnaire Development The original questionnaire was developed in Arabic and translated into English for the purpose of this study. To ensure the accuracy of the translation, the English version was reviewed for consistency with the original Arabic version. This research also employed the CROSS Checklist to ensure comprehensive reporting and adherence to best practices for online survey studies. 12 The questionnaire was developed collaboratively with pharmacy practice and research ethics experts and grounded in a comprehensive literature review. 13 – 15 It was designed to cover the four fundamental principles of biomedical ethics: autonomy, beneficence, non-maleficence, and justice. These principles guided the formulation of questions regarding ethical concerns related to AI integration in pharmacy practice. Comprising two parts (See Appendix I ), the first section gathered sociodemographic information from the students. In contrast, the second section included 12 items focused on collecting responses regarding pharmacy students’ perspectives on AI integration. The five-point Likert scale, ranging from “strongly disagree = 1” to “strongly agree = 5”, was used to gauge participant opinions. The questions were structured to explore issues such as the autonomy of pharmacy professionals in decision-making, the potential benefits and risks associated with AI use, and equitable access to AI technologies. The questionnaire language was simple Arabic. The Arabic version of the questionnaire was tested for content validity by a pharmacy faculty member with experience in pharmacy practice research and questionnaire development. The clarity and comprehensibility of the Arabic version of the questionnaire were further tested with a small group of pharmacy students, leading to minor revisions for the final version. Additionally, all questions on the survey platform are designed as mandatory responses to prevent the possibility of missing data or incomplete responses. Sample Size and Selection The minimum sample size for the study was calculated using Raosoft software. Based on a population size of 20,000 pharmacy students from diverse educational institutions in the MENA (Middle East and North Africa) region, the required sample size for a 95% confidence level and a 5% margin of error was determined to be 377 students. As the sample size remains relatively consistent for larger populations, this number was deemed sufficient to obtain a representative sample. Participants were recruited online, employing a convenience sampling technique via social media platforms (WhatsApp, Facebook, and LinkedIn groups). We targeted pharmacy student-specific groups, including those focused on pharmacy practical training and academic knowledge sharing, as well as groups from official pharmacy faculty student gatherings in Arab universities. A single post was made in each group to recruit participants, followed by a reminder post every month during the study period. Only students enrolled in a pharmacy program were invited to participate by using a brief screening question at the start of the survey, asking participants to confirm their enrollment in a pharmacy college. To maintain data integrity and prevent multiple submissions, the survey included email login restrictions, effectively minimizing the likelihood of respondents submitting more than one questionnaire. Ethical Considerations The study adhered to ethical guidelines and principles, receiving approval from the Scientific Research Ethics Committee at Zarqa University (Approval No. 54/2021/2022). Before participation, all subjects provided electronic informed consent, outlining the study’s purpose, voluntary nature, and the assurance of participant anonymity. No personally identifiable information was collected, and all data were stored securely to protect participants’ privacy. While email login restrictions were used to prevent multiple submissions, the survey was designed to maintain participant anonymity, as no email addresses were tied to specific responses. Data Analysis Statistical analyses were conducted using IBM SPSS software, version 27. Descriptive statistics were utilized for summarizing demographic data and attitude statement responses, including frequency (N), percentage (%), mean (M), and standard deviation (SD). Attitude score was calculated by summing the responses to the 12 Likert-scale questions to quantify students’ concerns regarding AI integration in pharmacy practice. The mean score was then calculated, with higher scores indicating more pronounced concerns. Inferential statistical tests were employed to identify significant variations and associations within the study groups, including independent t -tests, one-way ANOVA, Pearson’s correlation ( r ), and logistic regression. Odds ratios (OR) and 95% confidence intervals (CI) were calculated for each predictor variable. For the logistic regression analysis, the continuous attitude score was dichotomized using the sample mean as the cutoff point. Participants with scores above the mean were categorized as having a more negative attitude, while those at or below the mean were considered to have a less negative or more positive attitude. The significance level was set at p < 0.05 for all statistical tests. Cronbach’s alpha was calculated to assess Likert-scale items’ internal consistency and reliability, with a value of 0.902. Results Demographic Characteristics A total of 702 pharmacy students, representing diverse demographic backgrounds, participated in the study. Most students were from Jordan (N = 255, 36.3%), followed by Libya (N = 136, 19.4%), Egypt (N = 126, 17.9%), Lebanon (N = 112, 16.0%), Palestine (N = 67, 9.5%), and Saudi Arabia (N = 6, 0.9%). The average age was 21.9 years (SD = 2.9), with 511 females (72.8%) and 191 males (27.2%). Most participants were single (N = 624, 88.9%), and the distribution of monthly household income included 28.9% in the lower class, 36.6% in the middle class, and 34.5% in the upper class. This income was recorded in each country’s currency and subsequently classified based on World Bank data, which informed the classification into these three socioeconomic categories. The majority (N = 617, 87.9%) held the local nationality of their study country, while 12.1% were international students. Education and Employment Regarding education, the majority enrolled in governmental universities (55.6%), were pursuing a bachelor of pharmacy (53.3%), and were distributed across academic years. Cumulative GPA performance reflected a diverse range. Regarding employment status, most students were not working. Among those employed, the majority worked or trained in community pharmacies (76.1%), while 23.9% worked in other settings such as hospitals and research labs. Table 1 shows participants’ education and employment-related information. Table 1: Education and Employment Status of Pharmacy Students (N=702). Variables Categories N (%) High school Public school 452 (64.4) Private school 250 (35.6) University Governmental 390 (55.6) Private 312 (44.4) Academic major BPharm 374 (53.3) PharmD 308 (43.9) MSc 20 (2.8) Academic year First 28 (4.0) Second 59 (8.4) Third 186 (26.5) Fourth 256 (36.5) Fifth 134 (19.1) Sixth 19 (2.7) Master’s degree level 20 (2.8) Cumulative GPA performance category ** Excellent 156 (22.2) Very Good 334 (47.6) Good 186 (26.5) Fair 26 (3.7) Work status Not working 451 (64.2) Employment 125 (17.8) Internship or trainee 126 (17.9) Workplace * Community pharmacy 191 (76.1) Others 60 (23.9) Open in a new tab * This question allowed responses from those with active working status. ** This question categories were self-reported by participants based on students’ self-assessment. Technology Literacy and AI Familiarity Participants’ responses regarding tech-savviness of the 5-point Likert scale (knowledgeable or skilled in utilizing contemporary technology, particularly computers) revealed that 38.0% were neutral about having this skill, followed by very skilled/skilled (39.7%) and very unskilled/unskilled (22.2%). Also, familiarity with AI (general understanding of AI technology) varied, with 34.5% being neutral, 40.1% not familiar at all/slightly familiar, and 25.4% somewhat familiar/very familiar. Perceived Barriers Regarding AI Figure 1 shows participants’ perspectives on barriers and ethical issues related to AI integration in future pharmacy practice. Notable concerns were 56.2–70.8% of respondents agreed/strongly agreed with concerns/barriers. They included patient data privacy (62.0%), susceptibility to hacking (56.2%), AI replacing non-specialized pharmacists (69.3%), cost limitations (66.8%), access barriers (69.1%), and the absence of comprehensive legal regulation (68.1%). Additionally, participants emphasized the need for proper training (70.4%), expressed concerns about physicians’ reluctance (65.1%), and highlighted patient apprehension regarding AI (70.8%). Figure 1: Pharmacy Students’ Perspectives on Barriers and Ethical Issues Related to AI Integration in Pharmacy Practice (N=702). Open in a new tab Factors Influencing Attitudes The total attitude score had a mean of 3.9/5 (SD = 0.6). Of 702 participants, 52.1% were higher than the mean score and exhibited a more negative attitude (with scores above the mean), while the remaining 47.9% demonstrated a less negative or more positive attitude toward AI integration in pharmacy practice. Tables 2 and 3 provide a detailed analysis of parameters impacting the attitude score. Factors such as country of residence, age, university, academic year, cumulative GPA achievement, work status, technology literacy, and basic AI understanding were found to influence students’ attitudes. Participants from private universities, those progressing in higher academic years, with a higher GPA, actively participate in the working environment, and individuals with higher tech-savviness and AI understanding reported a more negative attitude towards AI. Age exhibited a slight positive correlation with attitude, indicating a minor increase in concern as age increased. Also, there is a weak positive correlation between the attitude score and tech-savviness ( r = 0.174, p < 0.001) and basic AI understanding ( r = 0.155, p < 0.001). Table 2: Variables/Parameters Affecting Pharmacy Students’ Attitude Scores Towards AI Integration in Pharmacy Practice. Variable Categories Mean ± SD p -value Country of residence Jordan 3.9 ± 0.6 <0.001 Egypt 3.9 ± 0.6 Lebanon 3.6 ± 0.7 Libya 3.8 ± 0.7 Palestine 3.8 ± 0.5 Saudi Arabia 4.1 ± 0.5 Gender Males 3.8 ± 0.7 0.169 Females 3.9 ± 0.6 Student Nationality Local 3.8 ± 0.6 0.073 International 4.0 ± 0.6 Marital status Single 3.9 ± 0.6 0.996 Married 3.9 ± 0.6 Monthly household income Lower class 3.9 ± 0.6 0.791 Middle class 3.8 ± 0.7 Upper class 3.9 ± 0.6 High school Public 3.9 ± 0.6 0.156 Private 3.8 ± 0.7 University Governmental 3.8 ± 0.7 0.004 Private 3.9 ± 0.6 Academic major Bachelor’s 3.8 ± 0.6 0.374 PharmD 3.9 ± 0.7 Academic year First and Second (Basics/Foundation) 3.6 ± 0.7 <0.001 Third (Intermediate) 3.8 ± 0.6 Fourth (Intermediate) 4.0 ± 0.6 Fifth (Intermediate) 3.9 ± 0.6 Sixth and Master’s level (Advanced/Specialized) 3.9 ± 0.6 Cumulative GPA performance category Excellent 4.0 ± 0.6 0.002 Very good 3.9 ± 0.6 Good 3.8 ± 0.6 Fair 3.5 ± 0.9 Work status Not working 3.8 ± 0.7 0.021 Employment 4.0 ± 0.6 Internship 3.8 ± 0.6 Workplace Community Pharmacy 3.9 ± 0.6 0.668 Others 3.9 ± 0.6 Tech-savviness Very unskilled 3.7 ± 0.7 <0.001 Unskilled 3.7 ± 0.7 Neutral 3.8 ± 0.6 Skilled 3.9 ± 0.6 Very skilled 4.0 ± 0.6 Basic AI understanding Not familiar at all 3.7 ± 0.7 0.002 Slightly familiar 3.8 ± 0.6 Neutral 3.9 ± 0.6 Somewhat familiar 4.0 ± 0.6 Very familiar 4.0 ± 0.6 Open in a new tab Table 3: Correlation Analysis of Key Variables with Total Attitude Scores Towards AI Integration in Pharmacy Practice. Variable M ± SD Pearson Correlation Coefficient ( r ) 95% Confidence Interval (CI) p -value Age 21.9 ± 2.9 0.089 (0.015 – 0.162) 0.018 Tech-savviness 3.3 ± 1.1 0.174 (0.101 – 0.244) <0.001 Basic AI understanding 2.8 ± 1.1 0.155 (0.082 – 0.227) <0.001 Total attitude score 3.9 ± 0.6 1 - - Open in a new tab Table 4 highlights logistic regression analyses that investigated the impact of these various factors on the attitude score related to AI integration in the pharmaceutical field. Participants from the lower and middle classes had higher odds of having a more negative attitude toward AI integration compared to those from the upper class. Second- and fourth-year students showed higher odds of negative attitudes towards AI integration than first-year students. Second-year students had the highest odds ratio (OR = 21.3, 95% CI: 1.14–396.85). Participants who disagreed with being tech-savvy had lower odds of holding a negative attitude towards AI integration compared to those who strongly agreed with being tech-savvy (OR = 0.2, 95% CI: 0.057–0.731). Table 4: Logistic Regression Analysis of Factors Associated with Pharmacy Students’ Attitudes Towards AI Integration in Pharmacy Practice. Categories Sub-Categories OR 95% CI. for OR p -value Lower Upper Country of residence Jordan 0.943 0.295 3.009 0.920 Libya 1.307 0.366 4.660 0.680 Lebanon 0.523 0.137 1.990 0.342 Egypt 0.830 0.218 3.156 0.784 Palestine (REF) - - - 0 Gender Male 0.981 0.452 2.133 0.962 Female (REF) - - - 0 Student Nationality Local 2.124 0.695 6.497 0.187 International (REF) - - - 0 Age 1.106 0.971 1.260 0.130 Marital status Single 1.592 0.595 4.257 0.354 Married (REF) - - - 0 Monthly household income Lower Class 2.840 1.091 7.390 0.032 Middle Class 2.389 1.087 5.248 0.030 Upper Class (REF) - - - 0 High school Public 1.024 0.466 2.253 0.952 Private (REF) - - - 0 University Governmental 0.564 0.266 1.197 0.136 Private (REF) - - - 0 Academic major BPharm 0.136 0.007 2.655 0.188 PharmD 0.342 0.020 5.737 0.456 MSc (REF) - - - 0 Academic year 1st 0.000 0.000 - 1.000 2nd 21.272 1.140 396.849 0.041 3rd 7.340 0.538 100.213 0.135 4th 16.402 1.257 214.007 0.033 5th 7.306 0.509 104.833 0.143 6th (REF) - - - 0 Cumulative GPA achievement Excellent 0.152 0.009 2.603 0.194 Very good 0.130 0.008 2.154 0.154 Good 0.060 0.003 1.022 0.052 Fair (REF) - - - 0 Work status Not working 1.487 0.682 3.242 0.319 Other (REF) - - - 0 Workplace Community Pharmacy 0.915 0.410 2.041 0.828 Other (REF) - - - 0 Tech-savviness Strongly Disagree 0.720 0.063 8.179 0.791 Disagree 0.205 0.057 0.731 0.015 Neutral 0.385 0.148 0.999 0.050 Agree 0.398 0.151 1.051 0.063 Strongly Agree (REF) - - - 0 Basic AI understanding Strongly Disagree 1.257 0.254 6.211 0.779 Disagree 1.253 0.311 5.049 0.751 Neutral 1.430 0.396 5.160 0.585 Agree 0.975 0.280 3.394 0.968 Strongly Agree (REF) - - - 0 Open in a new tab Discussion The present study’s results shed light on pharmacy students’ perspectives regarding integrating AI into their future practice, primarily focusing on ethical considerations. The ethical use of AI and ChatGPT in pharmacy practice has been the subject of numerous research studies. 6 , 16 – 20 They emphasized the importance of ethical guidelines, the reliance of healthcare providers on AI assistance, and the significance of incorporating ethics into practice. 6 , 16 – 20 There is a known conflict between the advantages of AI and privacy and human rights issues, highlighting the need for continued development of moral AI integration. 21 Participants expressed various concerns and barriers related to AI integration in pharmacy practice. Notably, concerns regarding patient data privacy, susceptibility to hacking, potential replacement by AI systems, and cost limitations hindering accessibility were prominent. These concerns align with previous research that highlights the importance of data privacy, cybersecurity, and equitable access to AI technologies in healthcare settings. 22 , 23 Additionally, participants emphasized the need for comprehensive legal regulations governing the use of AI in pharmacy practice, proper training to utilize AI effectively, and addressing physicians’ reluctance and patient apprehension toward AI technologies. These findings underscore the importance of regulatory frameworks, education, and stakeholder engagement in promoting the responsible integration of AI in healthcare. 24 Research in various developing countries, including the UAE, Pakistan, Jordan, and Palestine, indicates positive attitudes of pharmacy and medical students towards AI in healthcare and education. 6 , 16 – 20 Nevertheless, issues with over-reliance, moral implications, ignorance, and inadequate training still persist. Students emphasize the importance of addressing ethical issues, including the integration of AI instruction in the curriculum and promoting responsible AI use in academia and practice, while also acknowledging the potential benefits of AI. This aligns with our findings on pharmacy students’ concerns about AI, suggesting a shared sentiment across healthcare disciplines regarding the potential benefits and challenges of integrating AI into pharmacy practice. The current study differs from previous studies in that it focuses specifically on pharmacy students as future pharmacy practitioners, providing a comprehensive analysis of their attitudes toward the use of AI in their future practice. A previous study conducted across 12 countries, particularly in Europe, revealed predominantly positive attitudes towards AI among international pharmacy students, who expressed a strong desire for more AI education but largely felt unprepared for its application in their future roles. 5 In contrast, our study found a higher prevalence of concerns regarding AI’s integration into pharmacy practice. This discrepancy may be due to differences in the sample populations, educational systems, or regional/cultural contexts. For example, given the relatively low familiarity (25%) of our survey participants with AI, students in our study may have greater fears/concerns about exposure to the unknown/potential ethical challenges and practical limitations of AI, leading to heightened concerns about its impact on their future roles. In addition, a recent review has examined the impact of cultural identity in shaping how individuals integrate AI in the self in relation to others and determining the effect of AI on key decision-making processes. 25 Our study revealed a generally negative attitude among participants toward AI integration in pharmacy practice. Factors such as country of residence, university type, academic year, cumulative GPA achievement, work status, technology literacy, and basic AI understanding significantly influenced participants’ attitudes. Although initial analysis did not show a significant difference in attitude scores by income, logistic regression analysis revealed that students from lower and middle-income households were more likely to express negative attitudes toward AI. This discrepancy can be explained by the multivariable nature of the logistic regression, which accounts for other influencing factors (controlling for confounders). Therefore, while income alone did not show a direct effect, it becomes significant when combined with other predictors. These findings contradict a prior study suggesting that individuals with higher technology literacy may exhibit more positive attitudes toward AI. 19 This indicates a need for targeted interventions to address concerns and promote positive attitudes among pharmacy students, particularly those with higher academic achievement and technological proficiency. These identified factors also reveal several noteworthy associations that provide insights into the dynamics of these concerns among pharmacy students. The discrepancy between countries might be influenced by diverse cultural, educational, or contextual factors in these regions. Interestingly, gender did not emerge as a significant predictor, suggesting that concerns about AI ethics are relatively uniform across male and female students. This did not align with our previous research, which indicated a different level of awareness and interest in AI-related topics among students. 19 The lack of a gender effect suggests a shared perception of the ethical implications of AI in pharmacy practice. Second- and fourth-year students showed significantly higher odds of negative attitudes toward AI integration compared to first-year students. A possible explanation for these findings could be related to differences in their exposure to AI and clinical practice. Second-year students may be more aware of the potential challenges AI poses to traditional pharmacy roles but not yet fully understand its potential benefits, which may result in a more cautious or negative outlook. By the fourth year, students might have more practical exposure yet concerns about job displacement or the role of AI in their future careers may become more pronounced. These findings suggest that workforce transition assistance programs in universities could also provide support to students as they adapt to new roles, influenced by both academic content and practical experiences. Economic factors alongside other variables can influence individuals’ perceptions of emerging technologies and care must be taken when interpreting income effects. Addressing these concerns is essential to ensure inclusive and equal opportunities for students from diverse socioeconomic backgrounds. Academic factors, including university, academic year, and GPA, also shaped AI ethics concerns. These findings underline the need for targeted educational interventions, tailoring AI ethics discussions to students’ specific academic context and progression. Another notable aspect is the influence of technology literacy and AI understanding on concerns. Students who reported higher levels of modern technological familiarity expressed greater concerns about the ethical implications of AI integration in pharmacy practice. This indicates that even those with higher technological literacy may have heightened ethical apprehensions about AI’s role in the profession. To mitigate these impacts, it is essential to explore strategies such as reskilling and upskilling programs that equip future pharmacists with the skills necessary to thrive in a changing job market. Additionally, regulatory measures should be introduced to ensure that the benefits of AI adoption are equitably distributed, minimizing the societal risks associated with job displacement. From a philosophical standpoint, both utilitarian and deontological approaches provide valuable frameworks for understanding AI’s ethical implications in pharmacy education and practice. 26 Utilitarianism supports maximizing AI’s benefits—like improved efficiency and personalized care—while managing potential harm, such as erosion of trust if patient privacy breaches and income inequality due to job displacement. 27 Deontology, in contrast, emphasizes upholding ethical principles (autonomy, fairness, and confidentiality) in patient care and addressing worker concerns, such as job security and reskilling opportunities. 28 This balanced approach ensures that AI’s integration aligns with both practical and ethical considerations, maximizing positive societal impact. Limitations of the Study and Future Research The study sample may not represent the entire pharmacy student community from all MENA countries, as the present study sample only included five countries from the region. This could limit the generalizability of the findings. Yet, the selection of the countries included in the present study was made to represent the subregions within the MENA, including countries from the Levant, the Arabian Gulf, and North Africa, thus representing the different sociodemographic sub-regions within the MENA. As the case of any survey, negative wording could influence the study participants’ opinions and choices. In the present study, all efforts were made to avoid potentially negative wording/expressions via the changes made during face and content validation processes. Additionally, the survey relied on self-reported concerns, which could be subject to response bias. Convenience sampling via social media platforms is a major drawback of another study, as it may lead to substantial sampling bias. Future studies could benefit from more diverse and larger samples. We also acknowledge that translation nuances may impact on the participants’ responses in how they understood and interpreted certain questions. Future research could conduct in-depth interviews to gain a more comprehensive understanding of the ethical concerns and perspectives of pharmacy professionals and stakeholders. Longitudinal studies could track changes in attitudes over time as AI becomes more integrated into pharmacy practice. Comparative studies across different countries could highlight variations in concerns and the readiness of varying healthcare systems for AI integration. These studies are investigating the effectiveness of specific interventions, such as education programs, in mitigating ethical concerns and improving the responsible implementation of AI in pharmacy practice. Conclusion The findings of the current study suggest that pharmacy students express significant concerns regarding AI integration into practice, particularly around ethical challenges and its impact on their future roles. These concerns indicate a need for continued, targeted AI education within pharmacy curricula, focusing not only on the technological aspects but also on ethical and practical implications. By addressing the identified concerns and promoting ethical AI practices, pharmacy education and practice can harness AI’s potential to enhance patient care, improve outcomes, and advance the profession. Acknowledgments We would like to thank all pharmacy students for their valuable contributions. We also would like to express our gratitude to Dr. Qosay Al-Balas and the biomedical ethics master cohort—Maram, Roa’a, Mona, Sana, Ola, and Bayan—for their insightful discussions and constructive comments, which greatly contributed to the development of this work. Funding This research was partially supported by “The Research Ethics Education Program” through a grant from NIH, USA (1R25TW010026-01). The funder played no role in the design of the study, collection, analysis, interpretation of the data, or writing of the manuscript. Appendix Section 1: Demographic Information Country of Residence: Jordan Libya Egypt Lebanon Palestine Saudi Arabia Age: ____________ Gender: Female Male Marital Status: Single Married Household Income (Please select the category that best represents your family’s monthly income according to your country’s wages classification system): Lower class Middle class Upper class ╲ Jordanian Dinar Libyan Dinar Egyptian Pounds Lebanese Pounds Shekels Saudi Riyal Lower class <500 <1000 <5,000 <1,000,000 <1,850 <10,000 Middle class 500–1000 1000–2000 5,000–10,000 1,000,000–5,000,000 1,850–3,700 10,000–15,000 Upper class >1000 >2000 >10,000 >5,000,000 >3,700 >15,000 Open in a new tab Nationality: Local (National studen) International (Non-local student) High School Type: Public school Private school University Type: Governmental Private Academic Major: Bachelor of Pharmacy (BPharm) Doctor of Pharmacy (PharmD) Master of Science (MSc) Academic Year: First year Second year Third year Fourth year Fifth year Sixth year Master’s degree level Cumulative GPA Performance: Excellent Very Good Good Fair Current Work Status: Not working Employed Internship or trainee Workplace (If employed or an intern): Community pharmacy Hospital Research lab Other (Please specify): __________ How would you rate your overall knowledge or skill in using contemporary technology (particularly computers)? Very unskilled Unskilled Neutral Skilled Very skilled How familiar are you with Artificial Intelligence (AI) technology? Not familiar at all Slightly familiar Neutral Somewhat familiar Very familiar Section 2: Perceptions on Barriers and Ethical Issues Regarding AI Integration Please indicate your level of agreement with the following statements regarding AI integration in pharmacy practice (1 = Strongly Disagree, 5 = Strongly Agree). Statement Strongly Disagree Disagree Neutral Agree Strongly Agree The integration of AI in pharmacy practice may pose a potential risk to patient data privacy. □ □ □ □ □ There are concerns regarding the susceptibility of AI systems in pharmacies to hacking and cybersecurity threats. □ □ □ □ □ AI systems may gradually replace non-specialized pharmacists. □ □ □ □ □ The requirement of costly subscriptions for AI systems limits their adoption. □ □ □ □ □ Limited access to AI technologies may be considered a barrier in the field of pharmacy practice. □ □ □ □ □ There is an absence of comprehensive legal regulations governing the use of AI in pharmacy practice. □ □ □ □ □ There is a need for proper training for pharmacists to effectively utilize AI in their professional practice. □ □ □ □ □ Physicians may be reluctant to embrace AI within the realm of pharmacy practice. □ □ □ □ □ Patients may express concerns about the ability of AI to generate suitable treatment plans. □ □ □ □ □ AI may impact the time allocated for patient counseling due to its limited communication skills and lack of ability to interpret body language. □ □ □ □ □ There is potential for AI systems to oversell unnecessary over-the-counter medications and cosmetics to patients. □ □ □ □ □ Educating AI developers about data privacy and ethics is essential for the responsible integration of AI into the healthcare sector. □ □ □ □ □ Open in a new tab Footnotes Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate This study was performed in accordance with the principles of the Declaration of Helsinki. The questionnaire and methodology were approved by the Clinical Pharmacy Department and the Research Ethical Committee at Zarqa University under the reference number (54/2021/2022). Informed consent was obtained from all participants. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the author(s) used ChatGPT 4o to assist with language and grammar editing. After using this tool, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. 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