Number
827
Name
Prompt to Performance: Implementing a Multi-School AI-Empowered Basic Science Ecosystem with Parallel Training
Date & Time
Monday, June 8, 2026, 6:00 PM - 7:30 PM
Location Name
Oglethorpe Ballroom
Speakers
Authors
Renu Agnihotri, ATSU
Matheu Wong, ATSU
Hermandeep Sandhu, ATSU
Christopher Wong, ATSU
Yohei Norimatsu, ATSU
Bill Miller III, ATSU
William Brechue, ATSU
Presentation Topic(s)
Technology and Innovation
Description
PURPOSE.
Generative AI and users may effectively collaborate through strategic
integration of key AI-MedEd tools into early medical/dental education. This
project outlines a longitudinal, multi-phase methodology designed to
implement and assess a university-wide AI initiative across multiple
institutions in early medical and dental education. The core objective is to
cultivate an equitable and secure AI-empowered environment to augment student
learning outcomes and enhance faculty productivity.
METHODS.
The project employs a cyclical four-phase ecosystem: Training, Application,
Evaluation, and Integration. The Foundational Training phase (during Fall
2025), focused on the acquisition of core skills. To inform resource
development, a comprehensive pre-training survey was administered to faculty
and student participants. This phase was realized through a multi-session,
parallel workshop series offered specifically to students (Year 1-2) and
faculty. Pre- and post-surveys and comprehensive data capture were used to
measure outcomes. The project now enters the Application phase.
RESULTS.
Initial survey findings from 143 students and 93 faculty affirmed an early
adoption trend centered on academic tasks vital to preclinical training.
Crucially, the feedback underscored a unanimous demand for institutional
policies concerning data safeguards (66%) and ethics of use (59%). The
parallel workshop series addressed specific needs. Faculty sessions focused
on pedagogical approaches for integration. Student sessions centered on using
tools for learning (e.g., NotebookLM, AI Flashcards and AI Notetaking).
CONCLUSIONS.
The application phase shifts the focus from core skill acquisition toward
active, curricular application, moving the project from "Prompt to
Performance". The long-term objective is to systematically measure the
impact of AI adoption. This multi-phase methodology, beginning with informed
training and moving into structured application and evaluation, serves to
systematically establish a core digital competency for future physicians and
dentists.
Generative AI and users may effectively collaborate through strategic
integration of key AI-MedEd tools into early medical/dental education. This
project outlines a longitudinal, multi-phase methodology designed to
implement and assess a university-wide AI initiative across multiple
institutions in early medical and dental education. The core objective is to
cultivate an equitable and secure AI-empowered environment to augment student
learning outcomes and enhance faculty productivity.
METHODS.
The project employs a cyclical four-phase ecosystem: Training, Application,
Evaluation, and Integration. The Foundational Training phase (during Fall
2025), focused on the acquisition of core skills. To inform resource
development, a comprehensive pre-training survey was administered to faculty
and student participants. This phase was realized through a multi-session,
parallel workshop series offered specifically to students (Year 1-2) and
faculty. Pre- and post-surveys and comprehensive data capture were used to
measure outcomes. The project now enters the Application phase.
RESULTS.
Initial survey findings from 143 students and 93 faculty affirmed an early
adoption trend centered on academic tasks vital to preclinical training.
Crucially, the feedback underscored a unanimous demand for institutional
policies concerning data safeguards (66%) and ethics of use (59%). The
parallel workshop series addressed specific needs. Faculty sessions focused
on pedagogical approaches for integration. Student sessions centered on using
tools for learning (e.g., NotebookLM, AI Flashcards and AI Notetaking).
CONCLUSIONS.
The application phase shifts the focus from core skill acquisition toward
active, curricular application, moving the project from "Prompt to
Performance". The long-term objective is to systematically measure the
impact of AI adoption. This multi-phase methodology, beginning with informed
training and moving into structured application and evaluation, serves to
systematically establish a core digital competency for future physicians and
dentists.