Chia-Yen Lu

Taiwan Adventist Hospital , Taiwan

Abstract Title:Impacts of Chatbot with Concept Mapping Features on Nursing Staff’s Learning Performance in Resuscitation Medications Training

Biography:

Chia-Yen Lu has dedicated nearly 20 years to clinical nursing, continuously advancing her expertise in critical and acute care while striving for ongoing personal and professional growth. She currently serves as the Head Nurse of the Surgical Ward at a regional teaching hospital. In 2023, she earned her Master’s degree in Healthcare Education and Digital Learning from National Taipei University of Nursing and Health Sciences. Her outstanding contributions and leadership were further recognized in 2024, when she was honored as both an Outstanding Nurse of Taipei City and an Excellent Supervisor.

Research Interest:

Since medication safety was subsumed under the global patient safety priority as the core content in 2004, the nursing staff’s competence in handling emergency medications has become critical. Nurses perform high-frequency medication operations on a daily basis and are required to be able to interpret prescriptions immediately. However, high-pressure clinical environments often lead to knowledge gaps in emergency medication administration, which increases risks of medication errors and litigation. Studies indicate that systematic training integrating clinical practice and theories can enhance structured medication knowledge.
Objective：This study developed an emergency medication curriculum based on advanced cardiac life support guidelines and evaluated two chatbot-based learning strategies: concept mapping versus traditional text-based tests, focusing on learning outcomes, motivation, and critical thinking.
Methods：A quasi-experimental study enrolled 110 nurses from a northern Taiwanese teaching hospital, randomized into experimental (n=55) and control (n=55) groups. Both groups learned cardiac arrhythmia medications via chatbot, with the experimental group utilizing visual concept mapping tests featuring progressive prompts (text hints for initial errors, supplementary materials for repeated errors), while the control group used standard text-based assessments.
Results：Both groups significantly improved in immediate post-tests (p<0.01), but the experimental group demonstrated superior delayed test performance (p<0.001), indicating enhanced knowledge retention. The concept mapping group showed greater improvements in learning motivation(p<0.05) and critical thinking(p<0.001), alongside higher technology acceptance scores (t=2.430, p< 0.05).
Conclusion：Chatbot-driven concept mapping significantly enhances nurses’ emergency medication knowledge retention and clinical decision-making in high-stress environments. The visual scaffolding and structured knowledge representation offer pedagogical advantages over traditional methods. These findings support broader implementation in emergency care training and interdisciplinary medical education.