A case study from Bold Quest 25 will be presented at I/ITSEC 2026 in the research “Structuring Observer Trainer Feedback for After-Action Reporting Using xAPI and Large Language Models.”
The study examines how Observer Trainer comments collected during a complex military medical exercise can be transformed into structured, traceable data that are easier to analyze and use during After-Action Reporting.
The approach combines xAPI-based data standardization with large language models to organize qualitative observations, identify recurring themes, and help generate Sustain/Improve insights while maintaining a connection to the original exercise evidence.
The research addresses a familiar challenge in large exercises: valuable observer feedback is often extensive, distributed, and difficult to synthesize quickly enough to support timely learning. By structuring this information more effectively, the workflow can help evaluators and Lessons Learned teams make better use of the observations already being collected.
Rather than replacing human judgment, the approach is designed to strengthen it by giving evaluators a faster and more transparent way to move from individual comments to actionable After-Action Review insights.