Exploring the use of generative AI advice for the academic advancement of faculty

Abstract

Abstract The SingHealth Duke–NUS Academic Medical Center manages over 2,800 clinical faculty members and processes over 400 appointments and promotions annually. The current Promotion and Tenure documentation includes over 30 documents, making it difficult and time-consuming for the faculty to locate specific appointment information. We developed “AskADD” in response to requests for clearer academic career development guidance. This study reports initial alpha testing and subsequent beta testing with 35 faculty members using AskADD. AskADD aids the faculty—physician-educators, physician-scientists, physician-innovators, and physician-leaders—in navigating academic career paths while increasing transparency and trust in appointment, promotion, and tenure processes. Our AI-integrated systems, initially tested using low or no-code Microsoft platforms and later developed with a custom GPT, deliver contextualized responses to promotion and tenure queries. The faculty and staff participated in user testing, providing feedback for improvements. Alpha and beta testing conducted with the same group of users indicated that a substantial portion of participants found the tool beneficial; suggestions were given for further refinement. Our experience contributes to the limited literature on AI-driven faculty advancement in academic medical centers and offers a novel paradigm for academic career support.

Ähnliche Arbeiten