Measuring adoption is not an end in itself: it is a lever for understanding how your collaborators learn and what engages them. A recurring user signals perceived usefulness. A drop in engagement can indicate unsuitable content, insufficient communication, or an unmet need.

This data transforms Didask AI management into a proactive approach: you are no longer only reacting to problems - you anticipate needs and adjust support before engagement drops.

Statistics are accessible in the Didask AI section > AI statistics. Data updates in real time and consolidates usage from both the learning assistant and the coach.

The dashboard presents three essential metrics:

The messages-sent chart lets you visualize trends over the selected period, identify activity peaks linked to training or communication campaigns, and detect engagement drops that require action.

Filtering options by period: from the beginning, last 7 days, last month, or a custom period with start and end dates.

💡 Use short periods (week or month) to analyze recent trends, and longer periods to measure overall progress.

For each active collaborator, you can see their last activity, number of conversations initiated, and total volume of messages sent.

This view lets you identify three profiles:

CSV export lets you download messages sent and the user list with their statistics, for in-depth analysis in your reporting tools or presentation to teams.

Keywords: statistics, usage, Didask AI, coach, learning assistant, monitoring, adoption, dashboard.