Background Artificial intelligence (AI) is rapidly reshaping healthcare, including pharmacy practice through drug interaction screening or treatment monitoring applications. In internet-restricted settings such as Syria, access to most AI-based platforms and digital tools is limited, which may hinder their adoption in community pharmacy practice. However, adoption of AI in such contexts remains largely unexplored. Objective To evaluate the level of AI adoption among community pharmacists in Syria and compare their familiarity and openness toward AI technologies with that of pharmacy students. Methods A total of 400 participants based in Syria were included: 200 community pharmacists and 200 pharmacy students. Data were collected through a combination of paper-based surveys (pharmacists) and online questionnaires (students). To enhance understanding, a five-slide presentation explaining basic AI concepts was made available to participants unfamiliar with the topic. Results Nearly half of respondents (55%; n = 219) reported using AI tools in daily life, with ChatGPT being the most recognized (53%, n = 211). Pharmacists demonstrated higher AI knowledge than students (5.7% vs. 2.5%; χ 2 (4, N = 400) = 19.39, p = .001). However, students showed significantly greater willingness toward integrating AI into future practice (χ 2 (2, N = 396) = 33.35, p < .001). Pharmacists with either less than two years or more than six years of experience were less likely to use AI, whereas those with limited prior Generative AI knowledge appeared more willing to adopt such tools compared with those with higher prior knowledge (OR = 9.0, 95% CI 1.36–59.78, p = .023). Conclusion Our findings revealed a clear coexistence of interest in and concern about AI among both community pharmacists and pharmacy students. The study underscores the importance of developing clear regulatory guidance, structured training, and context-appropriate oversight to ensure the safe integration of AI into pharmacy practice, particularly in internet-restricted settings.
Kahwaji et al. (2026) studied this question.