Author: James Chun Lam Chow, Kay Li 👨🔬
Affiliation: University of Toronto, Princess Margaret Cancer Centre 🌍
Purpose: This study aims to integrate Equity, Diversity, and Inclusion (EDI) principles into AI-driven educational tools for medical physics. The goal is to create a chatbot framework that fosters accessible, personalized, and culturally sensitive learning experiences, ensuring that underrepresented groups can actively engage in medical physics education.
Methods: The proposed chatbot framework consists of several interconnected layers designed to deliver personalized and inclusive learning experiences. The User Interaction Layer serves as the entry point, where users input queries via text, voice, or accessible tools. The Natural Language Processing Layer interprets user input, ensuring diverse linguistic and contextual nuances are understood. The EDI Principles Module applies guidelines for EDI, ensuring responses are unbiased, culturally sensitive, and accessible. The Medical Physics Knowledge Base provides accurate educational content tailored to medical physics. The Personalization & Accessibility Layer adapts learning paths based on user preferences, such as language, learning style, or accessibility needs. A Feedback Loop continuously refines the chatbot by collecting user input to detect biases and enhance functionality. The Output Layer delivers responses in text, audio, or visual formats, optimized for inclusivity and user understanding.
Results: The implementation of this chatbot framework effectively addresses barriers such as linguistic diversity, varying learning styles, and accessibility needs. This approach ensures that all students, regardless of their background, can actively engage and succeed in medical physics education. The continuous feedback loop and personalized learning paths further enhance the chatbot's adaptability and effectiveness in bridging educational gaps.
Conclusion: This study demonstrates the significant impact of integrating EDI principles into AI-driven educational tools for medical physics. The proposed chatbot framework sets a new standard for leveraging AI to advance EDI in scientific and healthcare education, ultimately contributing to a more equitable and effective educational system.