Abstract
This study explores the development of an AI-driven educational website capable of dynamically updating its content based on user needs and medical trends to support the professional development of independent community pharmacists in Taiwan. Facing the challenges of an aging society and insufficient medical capacity, pharmacists are transforming from traditional dispensers to community healthcare providers. However, the current continuing education system remains hospital-centered and fails to adequately address the career development and practical needs of independent pharmacists, while course content lacks timeliness and differentiation. Guided by the Three Diamonds Model, this study adopted a case study approach and conducted semi-structured, in-depth interviews with seven pharmacists to collect data on their practical experiences and learning needs. Integrating insights from the World Health Organization’s Seven-Star Pharmacist framework and the draft of the Taiwan Pharmacist Core Competency Framework, a dynamic competency model tailored for independent community pharmacists was developed. The findings indicate that a pharmacist’s career can be divided into three stages: the Exploration Stage, the Professional Deepening Stage, and the Transition and Leadership Stage, each emphasizing different core competencies. Based on these findings, a dynamic learning support website prototype was built using Webflow AI and n8n, successfully achieving automated data integration and real-time content updating, thereby validating the feasibility of applying AI technology to enhance the agility and personalization of educational websites. Furthermore, the study found that future medical e-learning platforms should not only respond to public health trends but also serve as “trend sensors,” capable of proactively identifying pharmacists’ competency gaps and delivering relevant learning content. The contributions of this study include: (1) proposing a professional development support platform that responds to emerging health trends and addresses the practical challenges faced by primary-level community pharmacists; (2) establishing a trend-driven learning model for dynamic content updates, demonstrating its innovative and feasible potential as a future supplemental educational resource for community pharmacists in independent pharmacies; and (3) developing a design process for integrating AI technologies into asynchronous learning, providing a foundational reference for future expansion. Finally, this study suggests that future developments may incorporate Knowledge Graph (KG) technology to enhance the credibility and structured interconnection of medical information, ultimately building an intelligent and traceable medical knowledge engine.
Keywords
Healthcare design; Online learning platform; Dynamic content architecture; Career dynamic growth
DOI
https://doi.org/10.21606/iasdr.2025.746
Citation
Shen, Y.,and Tang, H.(2025) Establishing a Dynamic-Content Educational Website: A Case Study of Independent Community Pharmacists' Dynamic Growth in Taiwan, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.746
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 9 - Healthcare Design
Establishing a Dynamic-Content Educational Website: A Case Study of Independent Community Pharmacists' Dynamic Growth in Taiwan
This study explores the development of an AI-driven educational website capable of dynamically updating its content based on user needs and medical trends to support the professional development of independent community pharmacists in Taiwan. Facing the challenges of an aging society and insufficient medical capacity, pharmacists are transforming from traditional dispensers to community healthcare providers. However, the current continuing education system remains hospital-centered and fails to adequately address the career development and practical needs of independent pharmacists, while course content lacks timeliness and differentiation. Guided by the Three Diamonds Model, this study adopted a case study approach and conducted semi-structured, in-depth interviews with seven pharmacists to collect data on their practical experiences and learning needs. Integrating insights from the World Health Organization’s Seven-Star Pharmacist framework and the draft of the Taiwan Pharmacist Core Competency Framework, a dynamic competency model tailored for independent community pharmacists was developed. The findings indicate that a pharmacist’s career can be divided into three stages: the Exploration Stage, the Professional Deepening Stage, and the Transition and Leadership Stage, each emphasizing different core competencies. Based on these findings, a dynamic learning support website prototype was built using Webflow AI and n8n, successfully achieving automated data integration and real-time content updating, thereby validating the feasibility of applying AI technology to enhance the agility and personalization of educational websites. Furthermore, the study found that future medical e-learning platforms should not only respond to public health trends but also serve as “trend sensors,” capable of proactively identifying pharmacists’ competency gaps and delivering relevant learning content. The contributions of this study include: (1) proposing a professional development support platform that responds to emerging health trends and addresses the practical challenges faced by primary-level community pharmacists; (2) establishing a trend-driven learning model for dynamic content updates, demonstrating its innovative and feasible potential as a future supplemental educational resource for community pharmacists in independent pharmacies; and (3) developing a design process for integrating AI technologies into asynchronous learning, providing a foundational reference for future expansion. Finally, this study suggests that future developments may incorporate Knowledge Graph (KG) technology to enhance the credibility and structured interconnection of medical information, ultimately building an intelligent and traceable medical knowledge engine.