From Bottlenecks to Breakthroughs: Harnessing Generative AI for Faculty Disclosure Management Process

This session is a continuation of our highly rated ACEHP 2025 session "Generative AI in Action: 5-Step Framework to Improve Faculty Disclosure Management Process."  The faculty members were encouraged by the overwhelming positive feedback received during the session and in the evaluation forms and will be delving deeper into this critical process being undertaken by most accredited providers in Continuing Education in Health Professions (CEhp). 

Our hands-on, highly practical session in 2025 focused on introducing the five-step framework for analyzing the applicability of popular Generative AI (Gen AI) such as ChatGPT, Claude, and Perplexity in simplifying and improving CPD processes. In the past year, there have been several technological advancements in Generative AI tools with the proliferation of additional Gen AI tools gaining popularity. This overwhelming wealth of information makes it challenging for most Continuing Professional Development (CPD) providers to keep up with the advancements and determine their applicability to CPD processes. We have analyzed the attendee feedback from our ACEHP 2025 session and have explored the applicability of the latest advancements in Gen AI tools in solving some of the specific challenges discussed by the attendees. We will share our findings with the attendees and work collaboratively with various groups to further fine tune the applicability of Gen AI tools to simplify and streamline the disclosure process.  

Though the session focuses on faculty disclosure processes, the framework, methodologies, and analytical approaches discussed during the session are scalable to be applied for other CPD processes. 

The session will be broken down into four parts: 

Part 1: Popular Gen AI Advancements in 2025 

The faculty will discuss some of the key advancements in Generative AI tools and techniques. 

Part 2: Concept overview of selected advancements 

Based on the faculty’s experience working on disclosure data leading up to the ACEHP 2026 conference, the faculty will present key concepts that have proven to be useful to streamline the faculty disclosure process.  

The concepts aim to address the following key use cases: 

1. 
   

   Identifying conflict in faculty disclosures can sometimes be very nuanced and challenging. Can prompt engineering be applied to address these nuanced scenarios?  
   

Part 3: Case Studies 

The attendees will be presented with case studies, including sample disclosure data and problem statements. The attendees can work in groups to solve the case by applying the concepts discussed or applying their knowledge from other AI projects. 

Part 4: Discussion 

The attendees will share their experiences working on the case including success, challenges or learning experiences (aha moments!). The faculty will complement this learning by sharing results from their research leading up to the ACEHP 2026 conference.  

Part 5: Synopsis and Future Research  

The faculty will recap learnings from the audience and share other topics that may be worth reviewing as a continuation of the concepts discussed during the workshop. 

Key Takeaways 

The faculty will share the case studies, sample prompts and literature about various resources, thought leaders and knowledge base articles of various concepts discussed in the article.