Artificial Intelligence and Simulation-Based Learning in Pharmacy: Current Trends, Educational Outcomes, and Future Perspectives

AISIM26 Sağlık Profesyonellerinin Eğitiminde Yapay Zeka ve Simülasyon Kongresi, Trabzon, Türkiye, 4 - 07 Haziran 2026, ss.54, (Özet Bildiri)

Pharmacy education and pharmaceutical sciences have entered a profound transformation

period driven by digital transformation and artificial intelligence (Kavitkar et al., 2026; Phade

et al., 2026). Given that pharmaceutical technology requires high-cost, regulated laboratory

processes to convert theoretical knowledge into practical skills, this study systematically

reviews the integration of artificial intelligence and simulation technologies within pharmacy

curricula, cleanroom practices, and formulation development. Through a comprehensive

literature search (2019-2025) focusing on virtual reality-supported laboratory applications,

virtual patient simulations, and "in silico" modeling for drug formulation, it was found that

simulation technologies create a "safe-to-fail" environment that significantly enhances student

competency. Specifically, VR cleanroom simulations developed for USP 797/GMP standards

markedly improve students' knowledge and self-confidence in sterile preparation (AlMuzaini

et al., 2023). Virtual prescription dispensing and pharmacology simulation systems have proven

more effective than traditional methods in developing clinical decision-making skills

(Mohammed et al., 2026; Zheng et al., 2025). In pharmaceutical technology R&D, Finite

Element Analysis and Machine Learning algorithms have demonstrated a 20-30% reduction in

time and cost by optimizing formulation parameters, such as 3D printing features and drug

release kinetics (Abdullah et al., 2024; Sarabi et al., 2022; Yan et al., 2022). Furthermore, AI

optimizes student performance through personalized learning pathways and predictive analytics

(B et al., 2026; Phade et al., 2026). These technologies have evolved from supplementary

resources into a fundamental paradigm in pharmacy education and drug technology (Kavitkar,

Supalkar, A., et al., 2026; Kavitkar, Supalkar, Pachghare, et al., 2026). Integrating these tools

into the curriculum ensures standardization and safety while preparing students for the modern

pharmaceutical industry (Phade et al., 2026; Yang et al., 2023). Future perspectives suggest that

AI-based decision-making systems will become integral to clinical pharmacy practice, while in

silico simulations will dominate drug development processes.