Researcher ORCID Identifier

0009-0001-4998-5045

Date Degree Awarded

Spring 5-13-2023

Degree Type

Open Access Master's Thesis

Degree Name

Master of Science in Human Genetics and Genetic Counseling

First Thesis/Dissertation Advisor

Nicholas Gorman

Second Thesis/Dissertation Advisor

Daria Ma

Third Thesis/Dissertation Advisor

Sarah Kane

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Abstract

This study aimed to examine the attitudes and preparedness of genetic counseling program directors and faculty leadership in incorporating artificial intelligence and machine learning (AI/ML) into their curricula and its effect on core competency proficiency. AI/ML has been instrumental in creating and maintaining vital analytical tools and models employed by genetic counselors (GCs). However, research on the attitudes of faculty leadership in charge of training future GCs is limited. A nationwide survey conducted between November 2022 and February 2023 gathered 15 respondents holding diverse academic positions in genetic counseling program curriculum development. The majority of respondents had encountered AI/ML in academic settings, primarily through conference presentations (66.7%). They demonstrated neutral attitudes toward the challenges and limitations of integrating AI/ML into the curriculum, with an average mean score of 4.17 (SD = 1.61) on a 7-point Likert scale. Nevertheless, respondents somewhat disagreed that AI/ML integration is unnecessary (M = 3.57) and somewhat agreed that insufficient faculty expertise poses a potential barrier (M = 4.86). Respondents considered AI/ML to have the least impact on interpersonal, psychosocial, and counseling skills, highlighting the value of human expertise in these areas. No significant correlations emerged between program age and faculty members' perceptions of barriers and limitations to AI/ML integration. However, a positive correlation was observed between program age and the belief that AI/ML curriculum integration is unnecessary (r = 0.48). Despite low response rates and restricted generalizability, our findings indicate that AI/ML integration in genetic counseling education is in its infancy and requires further investigation and development. Future research should broaden the sample population, assess respondents' knowledge of AI/ML tools, and conduct in-depth interviews with program leadership to better comprehend factors influencing attitudes toward AI/ML curriculum integration.

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© 2023 Holmes D Kyla

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