Enhancing Deviation Management Education in Engineering: Leveraging Generative AI for Deviation Scenario Generation (74907)

Session Information: Innovation & Technology
Session Chair: Shin Yee Wong

Thursday, 28 March 2024 11:35
Session: Session 2
Room: Room 704
Presentation Type: Oral Presentation

All presentation times are UTC + 9 (Asia/Tokyo)

Deviation management plays a crucial role in the pharmaceutical industry, where strict quality control and regulatory compliance are paramount. Effective deviation management involves identifying, documenting, investigating, and resolving any deviations that occur. In teaching deviation management in an undergraduate engineering class, an important aspect is the creation of detailed scenarios that closely resemble real industrial deviations. This allows for in-depth analysis using the root cause analysis tools, and the development of appropriate solutions. There are two options for scenario creation. The instructor can either create a few scenarios for group projects or task the students with developing their own group-specific scenarios. The preferred approach is the self-creation method as it helps calibrate students' understanding of pharmaceutical Good Manufacturing Practices (GMP) and serves as an important exercise. However, this approach can be challenging since students often have limited industrial experience and may require extensive research and thinking to complete the task effectively.

One innovative approach to creating scenarios is by leveraging generative AI. The AI model has a vast dataset of historical deviations and knowledge of pharmaceutical GMP systems. ChatGPT, for example, can produce (somewhat) detailed scenarios that encompass various types of deviations, root causes, and potential impacts. Students can interact with the chat interface to refine the scenarios and make it authentic and realistic. Findings from the control group (without generative AI) showed that students are frustrated and spend most of the project hours on scenario creations. More data will be collected for the study group in Sep-Dec 2023.

Shin Yee Wong, Singapore Institute of Technology, Singapore

About the Presenter(s)
Dr Shin Yee Wong is a University Assistant Professor/Lecturer at Singapore Institute of Technology in Singapore

See this presentation on the full scheduleThursday Schedule

Conference Comments & Feedback

Place a comment using your LinkedIn profile


Share on activity feed

Powered by WP LinkPress

Share this Presentation

Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00