Qualitative Study Exploring Perceptions of Integrating Artificial Intelligence into Future Pharmacy Objective Structured Clinical Examinations
Year: 2025
Type: article
Abstract: OBJECTIVE: This qualitative study explored how pharmacy faculty and graduates perceive the potential future use of artificial intelligence (AI) in Objective Structured Clinical Examinations (OSCEs) at Qatar University, where AI had not yet been implemented. To reflect the open-ended nature of the evaluation, participants were asked to consider multiple hypothetical ways AI might be incorporated across OSCE design, administration, assessment, and feedback, rather than any specific predetermined model. METHODS: Semi-structured interviews with 12 faculty members and 5 focus groups with 29 graduates were conducted using purposive sampling. Data were analyzed thematically using Braun and Clarke's reflexive approach. RESULTS: Five themes emerged: (1) Traditional OSCE Challenges, which included inconsistencies in grading, lack of standardization, logistical burdens, and limited feedback; (2) Perceptions of AI Use in Education and OSCEs, where AI was viewed as a supportive but not substitutive tool; (3) Potential Roles and Benefits of AI in OSCEs, highlighting its capacity to improve standardization, automate tasks, provide structured feedback, and reduce exam-related stress; (4) Potential Challenges and Concerns, including limitations in assessing empathy, fairness, algorithmic bias, privacy, and overreliance on AI; and (5) Implementation Strategies, emphasizing the need for piloting, ethical training, institutional readiness, and adoption of a combined human and AI model. CONCLUSION: Participants expressed cautious optimism about integrating AI into OSCEs. Although AI offers promising solutions to challenges, thoughtful and gradual implementation is essential. Institutional investments, stakeholder training, and strong policy frameworks are critical to ensuring AI enhances rather than compromises educational quality and integrity in pharmacy assessment.
Authors: Ziad G. Nasr, Abrar Mohamed, Hebatalla Almarri, Najwa Amir, Roaa Rustom, Lana Kattan, Sara Abulola
Institution: Qatar University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,400
OpenAlex ID: W4416528565
