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Meal

Clinical

Early Career Professional

Mid Career Professional

Senior Career Professional

DEIB Session

Micro-Credential: Promoting Critical Thinking

Micro-Credential: Facilitating Small Group Learning

Micro-Credential: Promoting an Inclusive and Humanistic Learning Environment

Micro-Credential:Developing a Teaching Philosophy

Micro-Credential: Technology Integration in Didactic Teaching

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Clinical

New Technologies

NT-005 - Institutions' strategies and protocols to promote effective and ethical uses of generative AI technology

Saturday, March 8, 2025

11:00 AM - 12:00 PM EST

Location: Maryland A, Ballroom Level

CE: 1

Keyword: Artificial Intelligence (NT)
SoTLFest Designation: Yes
LIAA Designation: No

Meixun Sinky Zheng, Ph.D.

University of the Pacific, Arthur A. Dugoni School of Dentistry
Bina Surti, D.D.S. MA.Ed.

University of the Pacific, Arthur A. Dugoni School of Dentistry
Hanann Tomeh, D.D.S., MPH

A.T. Still University Arizona School of Dentistry & Oral Health
Wil Abshier, D.M.D., M.S.

University of Louisville School of Dentistry

As generative AI (genAI) tools such as ChatGPT and Co-pilot are increasingly adopted, AI literacy has become an important competency for students and faculty. Institutions need to provide training to prepare faculty and students for AI integration and develop protocols to guide effective and ethical uses of this new technology. In this interactive seminar, presenters from three dental schools will share our institutional approaches to faculty and student training as well as our school protocols. You will gain a deeper understanding of AI ethics, as well as learn adaptable approaches for guiding effective uses of generative AI at your institution.

Learning Objectives:

Recognize the importance of generative AI literacy for dental faculty and students.
Identify different institutional approaches and protocols for supporting faculty and students' effective and responsible use of generative AI technology.
Develop strategies to address ethical issues such as bias, transparency, consent, privacy and re-identification risks associated with generative AI applications.