Construction of a Digital Twin Model for Pharmaceutical Education Driven by Transformative Learning — Innovative Practice of Chronic Disease Management Based on the Integration of Short Video, AI, and VR
Year: 2025
Type: article
Abstract: To address the pain points in pharmaceutical education, such as the shortage of practical teaching resources and inadequate training in complex medication scenarios, this study constructed a three-in-one practical teaching mode for pharmacy, including “short video – virtual simulation – intelligent navigation”. Based on the theories of microlearning and transformative learning, the study innovatively adopted the strategy of “cognitive load optimization + stepped task-driven” approach. The study dismantled the management of five major chronic diseases, including hypertension and chronic hepatitis B, into modular short videos. Additionally, the study introduced cutting-edge topics such as the visualization of the “DFI-DGCF algorithm for medicinal and dietary contraindications.” Through the WeChat public platform, the study established a closed-loop system of “learning – practice – evaluation – application”, integrating AI personalized navigation (recommending learning paths based on learning behavior analysis), VR pharmacy training (simulating 23 types of high-risk scenarios), and blockchain technology for ability certification. Practical application showed that this mode improved students’ accuracy in clinical decision-making. The study provided an empirical paradigm of theory-technology-ecology collaborative innovation for the digital transformation of pharmaceutical education. The “three-dimensional and nine-degree” evaluation system and UGC crowdsourcing mechanism constructed in this study offered replicable solutions for the high-quality development of medical education.
Source: International Education Forum
Authors: Suzhen Jiang, Yue Chen
Institution: Peking University People's Hospital
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4409204769