Every year, the Time for Class report produced by Tyton Partners gives us data we expected and data we didn’t. This year, it was the didn’t-expect pile that kept us talking.
The 2026 report, done in collaboration with D2L, draws on surveys of over 300 administrators and 1,500 instructors across more than 250 unique institutions. It’s one of the most comprehensive ongoing looks at how digital learning is evolving in higher education. This year it surfaces a tension that runs through almost every finding: institutions are working hard to respond to AI, but many are responding to the symptoms rather than the underlying causes.
To help unpack what stood out, we spoke with Dr. Cristi Ford, chief learning officer at D2L, and Dr. Emma Zone, senior director of academic affairs at D2L. Here are five findings that surprised us and what they mean for the people doing this work every day.
1. Administrators Are Now the Most Active Daily AI Users, Surpassing Students for the First Time.
For four consecutive years, students led every other group in daily AI adoption. That changed in 2026. Administrators are now using AI daily at a rate of 43%, compared to 32% of students. It is the first time admins have led since tracking began in 2023.
It’s a meaningful shift, and Dr. Ford thinks it’s largely intentional. “I remember a year ago talking to administrators of campuses who said, “We want to be an AI-first campus.’ And my call to action to them was, ‘Well, then you have to be fluent in the tools you’re asking your faculty and students to use.’ I think that shift is probably purposeful because there’s an appetite where before it was, ‘AI is here, it’s tentative, let’s wait and see.’ Now we’re in the era where AI is here to stay.”
Dr. Zone points to something else driving the change: the way AI entered people’s lives differently from every technology before it. “Any of the other technologies that you and I have worked with, they’re used in that context and that context only. What’s interesting about AI is that it’s so multifaceted: they might be using it to create a meal plan, a workout routine and a study plan. It’s being used to inform the teaching and learning moment in a way that’s completely unique.”
The data also shows that administrators who use AI personally are less worried about academic integrity as a barrier and more likely to invest in faculty capability. Personal experience with the tools, it turns out, is one of the strongest predictors of institutional readiness. What changed isn’t that leadership ordered AI into existence; it’s that the people using the tools stopped seeing it as a threat and more as a capability to build.
2. Faculty Cheating Concerns Have Nearly Doubled Since 2024. But the Data Suggests We May Be Solving the Wrong Problem.
Preventing student cheating has jumped from 36% to 55% as a top faculty challenge in just two years. That’s a striking rise. But the same data reveals something more complicated: students who use AI on a daily basis are actually less likely to report workload anxiety. And among students who would use AI even when it’s banned, career preparation, not laziness, is the primary motivator.
“Every time we talk about cheating,” says Dr. Ford, “I cringe a bit. The conversation around cheating is as old as teaching and learning has been part of our institutional systems. You go back 30, 40 years, people were cheating by purchasing a paper from someone on campus. The difference here is that we have to fundamentally shift how we think about evaluation and assessment. We can no longer just focus on these outputs and these products.”
“The conversation should be about how do we engage learners in ways that we can think about evaluation very differently where co-construction of learning can happen, where the process matters as much as the product.”
Dr. Zone adds a layer of nuance: as faculty become more familiar with AI outputs themselves, they’re better equipped to recognize them in student work, which may partly explain why cheating concerns are rising in parallel with adoption. “There’s a double-edged sword where people are at once excited by possibility and also having a moment of, ‘Oh, no.’ I think that’s coming out in the data around the cheating piece.”
The data doesn’t excuse academic dishonesty. But institutions focused primarily on detection and restriction may be responding to a symptom while the deeper pressure on students goes unaddressed.
3. Faculty and Administrators Think They Know What Students Are Struggling With. Students Tell a Different Story.
Faculty rank ineffective study skills as the number one student challenge (68%). Administrators agree, ranking it first at 53%. Students rank workload anxiety first (40%), followed by heavy course load (32%). “Underprepared,” which faculty place second, students rank ninth.
These aren’t minor differences; they’re different diagnoses that lead to different interventions. And the cost shows up in another number: fewer than 20% of students who faced mental health challenges this year accessed counseling through their institution, even though mental health has been the number one student life challenge for three consecutive years.
Dr. Zone frames it as a resource and design problem simultaneously. “One of the insights around the pressure students are feeling being a prerequisite to them seeking tools like AI to make things easier is something institutions really have to pay attention to. It all comes down to resource: whether that’s human resource, capital funds, investments into technology. And I think it is a moral obligation of institutions to be paying attention to that and be smart and intentional around how they’re actually doing that.”
Dr. Ford pushes on the resource assumption though: “We can’t make the assumption that just because students aren’t availing themselves to the resources available, those resources aren’t the right ones. Resources aren’t a monolith. Given your institutional context and mission and the kinds of students you serve, your resources may need to be curated very differently.”
What the data makes clear is that the gap between how institutions diagnose student struggle and how students actually experience it is wide enough to matter and narrow enough to close with intention.
4. 61% of Faculty Say They Embed Real-World Projects. 26% of Students Say They’ve Experienced One.
This may be the starkest number in the entire report. And it connects directly to something students are asking for: connecting their major to a career is the number one career readiness activity they want. Students who do participate in real-world projects are measurably more likely to say college is preparing them for a career (76% vs. 68% among those who haven’t).
Dr. Ford puts it plainly: “The fact that 61% of faculty say, ‘Yup, I’m doing that, I’m providing opportunities for my students to get these real-world projects,’ and only one in four students are saying they’ve actually had that approach — that is very telling of what we think we are doing versus what’s actually happening. Even if you think you’re doing it, it’s not what learners are taking away, and that’s the biggest challenge.”
Dr. Zone agrees and connects it to the broader assessment conversation: “That finding really stands out. It’s a big difference in terms of what faculty are suggesting they’re serving up and what students are saying they’re consuming. And it’s connected to AI: if we’re having real conversations about preparing students for the future of work, we also need to be having those conversations about experiential, problem-based approaches that we’ve long known are effective. But they’re historically difficult to design and even harder to assess.”
Dr. Ford adds one more layer: “The report also tells us that 47% of faculty and 61% of administrators say their job is changing because of AI. So, if you’re having a lived experience that your job has changed, yet in the classroom you’re not reinforcing that reality for students, we have to figure that out.”
5. Having an AI Policy Isn’t the Milestone. What Comes After Is.
Institutions with a formal AI policy have grown from 3% in 2023 to 32% in 2026. That is real progress. But one in two faculty doesn’t believe their institution’s AI policy is effective. And the data on what works is clear: faculty who integrate AI into assignments are more likely to use AI daily, report lower workload and see better student engagement than faculty who ban it entirely.
The report introduces three faculty personas:
- Integrators (24%), who redesign assessments around AI
- Defenders (23%), who revert to proctored formats
- Status Quo (54%), who haven’t significantly modified their practice.
The gap between Integrators and Defenders isn’t just in outcomes; it’s in philosophy.
Dr. Ford describes it this way: “Faculty who are more likely to be innovative and include AI in their evaluation and assessment: there’s a correlation between that and positive, pro-AI governance on their campuses. So, if you haven’t made a stance, or you’ve created policies that are prohibitive or created barriers around AI, and at the same time say you want to be an AI-first campus, there’s no correlation there.”
Dr. Zone makes the point that policy in this space can’t operate on the usual institutional timelines. “Because AI is changing so quickly, we cannot use the same frameworks for policy building that we have with other historically well-known policy frameworks. But if you have a campus where there are a lot of Status Quo or Defender faculty, it’s going to be harder to have those policy conversations at the implementation level, because people won’t really know how to give feedback if they haven’t experienced it.”
The institutions showing up in the data as getting this right aren’t the ones with the most comprehensive policy documents. They’re the ones where leadership uses AI, where faculty are encouraged to experiment, and where the policy evolves alongside practice. The Integrators in the data aren’t ahead because they have better documents: they’ve made AI intentional inside the work itself. This is what responsible-by-design looks like at the implantation level.
As Dr. Ford put it: “The moment is here and the moment is now. We can no longer just pour old wine in new bottles. That time is up.”
What Comes Next
What makes Tyton’s Time for Class report useful year after year is that it’s not just a snapshot of where things stand, but a record of which direction things are moving. The institutions showing progress aren’t waiting for perfect conditions. They’re making deliberate moves, and the data is starting to show what those moves look like.
For a perspective on how to leverage the data in the 2026 Time for Class report, we invite you to explore our companion digital site: From Adoption to Action: What the 2026 Time for Class Report Reveals About Higher Ed.
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