Number
505
Name
Faculty Burnout and Artificial Intelligence Adoption Readiness in Medical and Health Professions Education: A Pilot Cross-Sectional Study
Date & Time
Monday, June 8, 2026, 6:00 PM - 7:30 PM
Location Name
Oglethorpe Ballroom
Speakers
Authors
Lauren Lilley, Old Dominion University
Uzoma Ikonne, Old Dominion University
Ismail El Moudden, Old Dominion University
Sarah Shackelford, Old Dominion University
Presentation Topic(s)
Other
Description
Purpose
Burnout among faculty in health professions education is a significant
concern, yet there are few evidence-based interventions available. Artificial
intelligence (AI) technologies can provide potential solutions for managing
workload, but the link between burnout severity and willingness to use
AI-based interventions is unclear. This pilot study assessed the prevalence
of burnout among medical and health professions faculty and examined the
relationship between burnout levels and willingness to adopt generative AI
tools.
Methods
We conducted a cross-sectional survey of non-clinical teaching faculty at
Macon & Joan Brock Virginia Health Sciences at Old Dominion University
(EVMS IRB Protocol #24-12-XX-0284). Participants completed validated measures
assessing burnout across three dimensions (emotional exhaustion, depersonalization,
and personal accomplishment), AI knowledge, and adoption readiness. Bootstrap
correlation analyses with 2,000 iterations examined relationships between
variables, emphasizing effect-size estimation given the pilot sample size.
Results
Sixteen of 45 faculty completed the survey (35.6%). Most were women (13/16,
81.2%) and instructors with over ten years of experience (11/16, 68.7%). Most
participants (8/16, 50%) reported beginner level AI proficiency; a minority
(4/16, 25%) used AI tools for work often or always. Correlations showed that
fewer years of teaching were significantly associated with higher burnout (r
= –.51, p = .042). Furthermore, burnout with AI knowledge did not reach
statistical significance (r = 0.45, p = .079), indicating a medium effect
size. Faculty showed interest in learning more, noting potential benefits for
efficiency and instructional support despite limited use.
Conclusions
AI has become a promising tool for helping faculty manage workload. This
pilot study explored whether openness of AI adoption is related to burnout.
We found that early-career faculty reported more burnout and may be a key
group for targeted AI interventions. Future research with larger samples is
necessary to validate these findings and develop effective strategies for
implementing AI-based burnout solutions in academic medicine.
Burnout among faculty in health professions education is a significant
concern, yet there are few evidence-based interventions available. Artificial
intelligence (AI) technologies can provide potential solutions for managing
workload, but the link between burnout severity and willingness to use
AI-based interventions is unclear. This pilot study assessed the prevalence
of burnout among medical and health professions faculty and examined the
relationship between burnout levels and willingness to adopt generative AI
tools.
Methods
We conducted a cross-sectional survey of non-clinical teaching faculty at
Macon & Joan Brock Virginia Health Sciences at Old Dominion University
(EVMS IRB Protocol #24-12-XX-0284). Participants completed validated measures
assessing burnout across three dimensions (emotional exhaustion, depersonalization,
and personal accomplishment), AI knowledge, and adoption readiness. Bootstrap
correlation analyses with 2,000 iterations examined relationships between
variables, emphasizing effect-size estimation given the pilot sample size.
Results
Sixteen of 45 faculty completed the survey (35.6%). Most were women (13/16,
81.2%) and instructors with over ten years of experience (11/16, 68.7%). Most
participants (8/16, 50%) reported beginner level AI proficiency; a minority
(4/16, 25%) used AI tools for work often or always. Correlations showed that
fewer years of teaching were significantly associated with higher burnout (r
= –.51, p = .042). Furthermore, burnout with AI knowledge did not reach
statistical significance (r = 0.45, p = .079), indicating a medium effect
size. Faculty showed interest in learning more, noting potential benefits for
efficiency and instructional support despite limited use.
Conclusions
AI has become a promising tool for helping faculty manage workload. This
pilot study explored whether openness of AI adoption is related to burnout.
We found that early-career faculty reported more burnout and may be a key
group for targeted AI interventions. Future research with larger samples is
necessary to validate these findings and develop effective strategies for
implementing AI-based burnout solutions in academic medicine.