Presentation
Beyond the LMS: Designing AI-Enabled Performance Support in High-Risk Sociotechnical Systems
DescriptionIn high-risk sociotechnical systems, workforce readiness depends on the ability to apply knowledge effectively within dynamic, time-constrained environments—not merely to complete formal training. However, traditional Learning Management System (LMS) approaches remain linear, curriculum-driven, and optimized for compliance, often failing to support real-time performance during operational work. This gap is particularly evident in healthcare informatics during large-scale Electronic Health Record transitions, where workflow variability and disruption are unavoidable.
This presentation introduces a human factors–informed design approach that reframes training as competency-aligned performance support embedded directly within workflows. Leveraging advances in artificial intelligence, the approach decomposes organizational knowledge into modular components and dynamically delivers context-specific guidance at the point of need. The system integrates structured microlearning with task-based job aids, enabling just-in-time support while minimizing cognitive burden.
Attendees will gain practical design principles for building AI-enabled learning ecosystems that align with work-as-done, rather than work-as-prescribed. The approach demonstrates how organizations can scale expertise, reduce search and reconstruction effort, and improve consistency of performance without increasing training overhead. While grounded in healthcare informatics, these principles are broadly applicable across high-complexity domains including aviation, defense, and industrial operations.
This presentation introduces a human factors–informed design approach that reframes training as competency-aligned performance support embedded directly within workflows. Leveraging advances in artificial intelligence, the approach decomposes organizational knowledge into modular components and dynamically delivers context-specific guidance at the point of need. The system integrates structured microlearning with task-based job aids, enabling just-in-time support while minimizing cognitive burden.
Attendees will gain practical design principles for building AI-enabled learning ecosystems that align with work-as-done, rather than work-as-prescribed. The approach demonstrates how organizations can scale expertise, reduce search and reconstruction effort, and improve consistency of performance without increasing training overhead. While grounded in healthcare informatics, these principles are broadly applicable across high-complexity domains including aviation, defense, and industrial operations.
Contributors
Event Type
Lecture
TimeWednesday, October 21st5:10pm - 5:30pm PDT
Location