From Adoption to Action:

From Reacting
To Managing

The 2026 Time for Class data doesn’t tell a simple story. It tells a complicated one about institutions working hard to do the right thing, often without the information or infrastructure to do it as well as it wants to.

But the complications resolve into a pattern when you look at the institutions showing up well across multiple findings. The ones seeing better outcomes on AI adoption, student engagement, workforce readiness and retention aren’t doing more. They’re doing different things, and the difference is mostly about intentional design.

They’re designing AI policies that define the why and leave room for the how, rather than policies built around restriction. They’re designing assessments around what they want to measure, rather than around preventing the tools students are already using. They’re designing course experiences that make support resources visible, not just available. And they’re making data investments that result in tools faculty can act on, not just dashboards administrators can review.

What the data keeps returning to is a simple but demanding idea: that the institutions making progress aren’t the ones with the best resources or the most sophisticated technology. They’re the ones willing to look honestly at the gap between what they think they’re doing and what students are experiencing. That gap is closeable. But only if institutions stop managing AI and start designing with it.

This site was produced by D2L in collaboration with Tyton Partners.