Measuring AI Adoption in Practice: Moving Beyond Traditional Learning Metrics

The success of professional development sessions is often measured by participation rates and post-session satisfaction. But in the context of AI, these easier metrics fail to measure what truly matters: how work is changing.

This session explores a practical, in-depth approach to measuring AI professional development that moves beyond surface-level indicators and into meaningful signals of transformation. Drawing on the design of the AI intermediate training series, we will examine how to assess:
– Growth in prompt sophistication and depth of interaction.
– Shifts from exploratory use to task-level and workflow integration.
– Changes in human capacity through AI-enabled augmentation.
– Cultural signals, including collaboration and shared learning around AI

Participants will gain insight into the development of measurement tools that look at behavior change, task augmentation, and real-world application, while also navigating changes related to trust, psychological safety, and data collection.

This session is designed for professionals interested in moving from “Did they like it?” towards “Did it change how work gets done?” If we’re not measuring how work changes, we’re measuring attendance as opposed to AI adoption and its effects.