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    Putting the AI in Training: An Exploratory Study on a Dynamic Perfusion Scenario Tool

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    Author
    Lynch, Madison
    Issue Date
    2024
    Keywords
    Cardiopulmonary Bypass
    Chatbot
    Perfusion Education
    Scenario
    Simulation
    Advisor
    Wong, Raymond K.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Perfusion education stands at a pivotal juncture where integration of innovative technologies has the potential to enhance traditional education methodologies. The primary aim of this exploratory research project is to develop an interactive educational chatbot tool, using the artificial intelligence (AI) platform, Pickaxe, that engages users in perfusion scenarios. This tool is tailored to encourage use of active problem-solving and troubleshooting within the context of perfusion-related challenges. Upon implementation of the novel AI educational tool, survey response collection is expected to reveal specific areas for improvement, specifically pertaining to the chatbot’s efficacy, chatbot and scenario quality, tool usability, educational value, and the user’s overall impression. This project leverages the potential of AI and simulated learning to modernize perfusion education, providing a valuable and innovative tool for skill development. Results of analysis revealed significant correlations between the AI chatbot's perceived educational value and the chatbot’s tone, appropriateness, and accuracy of response. Differences in perceived educational value were observed between user roles, with students rating the chatbot’s educational value higher than professionals. Statistical analysis revealed a clear preference among users with high future use intentions, rating the chatbot's educational value significantly higher (mean = 4.67, SD = 0.52) than those with low future use intentions (mean = 3.29, SD = 1.25, p-value = 0.029). Thematic analysis from open-ended responses suggested opportunity for improvement in reduction of erroneous chatbot responses, scenario complexity, and the inclusion of visual components to improve the chatbot's educational impact.
    Type
    Electronic Thesis
    text
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Medical Pharmacology
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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