Presented By: Axel Rivas, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Co-Authors: Justin Atkins, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Daniel Levine, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Kaitlyn Novotny, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Savannah Schauer, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Edward Simanton, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Stephanie Wang, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas

Purpose 
Artificial intelligence (AI) can facilitate the transformation of educational media from one form to another for adaptation to the preferred learning style of medical students. Traditionally learning modalities split learning styles into visual, aural, reading/writing, and kinesthetic categories. Previous studies have shown some medical students prefer to learn through a combination of learning strategies. This may hinder their ability to learn the material if it is not presented in the preferred style. Current preclinical learning resources emphasize texts and videos which may be a disadvantage for kinesthetic and aural learners. AI tools possess the ability to simplify information and facilitate the conversion of educational media potentially aiding all forms of learning styles.

Methods 
We will survey currently enrolled medical students at the Kirk Kerkorian School of Medicine at UNLV to evaluate preferred learning styles. Learning styles will be self-identified into categories based on the VARK questionnaire. Additionally, we will also survey whether medical students are utilizing AI tools for the conversion of learning media to their preferred style.

Results 
We expect the learning styles amongst medical students to vary in preference based on the VARK categories. Consequently, we do not expect AI tools to be utilized frequently by medical students to transform learning media into their preferred learning style.

Conclusions 
We believe medical students are unaware of the functional way that AI can be utilized. Increasing awareness allows for the ability to incorporate AI into medical learning especially with the diversity among learning styles. This is especially important for students with different preferences of learning styles. Thus, utilizing AI to convert or tailor resources into preferred learning media has the potential to increase learning outcomes and foundational understanding. Further research is needed to determine if utilizing AI can increase learning outcomes such as class satisfaction.