Artificial Intelligence-Enabled Competency Assessment in Jordanian Pharmacy Programs: A Vision for the Future

Pharmacy Practice

  • Abeer M. Kharshid1Department of Clinical Pharmacy, Faculty of Pharmacy, Mutah University, Al Karak 61710, Jordan.
  • Zainab Z. Zakaraya2Department of Biopharmaceutics and Clinical Pharmacy, Faculty of Pharmacy, Al-Ahliyya Amman University, Amman, Jordan.
  • Mohammad Abu Assab3Clinical Pharmacy Department, Faculty of Pharmacy, Zarqa University, Zarqa, Jordan.
  • Alaa Abu Dayah4Department of Pharmaceutical Science, Faculty of Pharmacy, Jadara University, Irbid, Jordan.
  • Sofian Alwardat5Department of Medical Laboratory Sciences, Faculty of Allied Medical Sciences, Jadara University, Irbid, Jordan.
  • Mohammad Saleh1Department of Clinical Pharmacy, Faculty of Pharmacy, Mutah University, Al Karak 61710, Jordan.
  • Wael Abu Dayyih6Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Mutah University, Al Karak, Jordan.

Volume 24 Issue 2 Pages 1-4

DOI: 10.18549/PharmPract.2026.2.3473

Abstract

Background: The evolution of pharmacy education to competency-based education (CBE) demands novel assessment tools that are objective, prompt and scalable. Artificial Intelligence (AI) is considered a disruptive power in medical education worldwide, but its penetration in Jordanian pharmacy education is unassessed. Objective: To model the integration and impact of AI-enhanced competency assessment instruments in the education of pharmacy at a university and its college. Methods: Mixed-methods design was utilized. A virtual cohort of 200 students was used to simulate AI-proctored examinations at the Faculty of Pharmacy, Mutah University. Simulation results consisted of diagnostic accuracy, feedback quality, faculty burden, and student/faculty impressions. From the hypothetical focus group simulations, qualitative data was synthesized. Results: Simulated AI evaluations were more accurate than traditional evaluations diagnostically above threshold levels at 94% versus 76%, respectively, as well as provided immediate feedback and resulted in a 70% reduction in faculty time for grading. Students expressed relatively high levels of satisfaction with AI feedback clarity (4.6/5) and fairness (4.7/5). Faculty reported increased congruence with curriculum objectives and augmentation of personalized teaching. Conclusion: AI-based assessment systems had potential to enhance competency tracking, support high-quality feedback and be time efficient within Jordanian pharmacy education. A staged approach to real-world implementation is advised, along with training of stakeholders and regulatory support.

Keywords

  • Artificial intelligence
  • competency-based assessment
  • pharmacy education
  • Jordan
  • educational technology
  • adaptive learning
  • simulation
Pharmacy Practice

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