Qingran Lin
Papers
1
Total Citations
23
H-Index
1
About
Qingran Lin is a nursing education researcher whose work centers on integrating artificial intelligence into clinical training, with a particular focus on chatbot-assisted history-taking instruction. Her most-cited paper, a 2023 qualitative study using focus group interviews, conducted a critical needs assessment for developing chatbot-based history-taking programs for nursing students. This foundational work identified key pedagogical gaps and student preferences, demonstrating that learners themselves advocate for AI tools to master this complex clinical skill. By systematically analyzing student perspectives, Lin’s research provides an evidence-based framework for designing interactive, chatbot-driven simulations that enhance diagnostic reasoning and patient communication. Her contributions bridge the gap between educational technology and nursing practice, offering scalable solutions to a longstanding training challenge. With 23 citations to this pivotal study, Lin’s work is gaining traction among educators and curriculum developers seeking innovative, student-centered approaches. Her research not only highlights the potential of conversational agents in healthcare education but also sets the stage for future studies on AI’s role in clinical competency development.
Research Focus
Key Achievements
Top Papers
- 1