As students return to campuses this fall, many faculty are heading into the academic year with a familiar question and a new level of urgency. When AI can draft essays, summarize readings, generate code and answer discussion prompts, how can educators tell whether meaningful learning has taken place?
It’s a question many institutions are actively grappling with. According to the latest Time for Class findings from D2L and Tyton Partners, 47 percent of faculty say assessment design is the primary teaching practice they’re modifying because of AI, while 52 percent of students report that their instructors have already adjusted assessments in response.
The pace of change reflects a reality many faculty already recognize. AI is no longer a future consideration. It’s already influencing how students learn, study and complete coursework. As a result, assessment has become one of the most visible places where higher education is being asked to adapt.
Beyond the Cheating Conversation
Conversations about AI in education often begin with concerns about academic integrity. When students have access to tools that can generate polished outputs in seconds, traditional assignments can feel more difficult to evaluate.
Yet the research suggests that focusing solely on detection may miss a larger opportunity. Faculty responding to AI have largely split into three groups: Integrators, who redesign assessments around AI; Defenders, who rely more heavily on proctored formats; and a larger group that has made relatively few changes. The more notable finding is that faculty who take an integrative approach report better student engagement outcomes than those who focus primarily on restriction.
The distinction matters because it shifts the conversation away from whether students are using AI and toward what educators are trying to measure in the first place.
As Dr. Cristi Ford notes: “Every time we talk about cheating, I cringe a bit. The conversation around cheating is as old as teaching and learning has been part of our institutional systems. The difference here is that we have to fundamentally shift how we think about evaluation and assessment.”
Rethinking What Assessment Is Designed to Do
AI has exposed questions that existed long before generative tools entered the classroom.
If a student can successfully complete an assignment using AI assistance, what knowledge or skills was that assignment actually measuring? Was the goal to produce content? To demonstrate understanding? To apply concepts? To solve a problem?
Dr. Emma Zone frames the challenge this way: “What AI has really called into question is whether what we’ve been measuring ever actually measured what we intended to measure.” She adds that this creates an opportunity to design assessments that better evaluate critical thinking, the application of knowledge and the ability to work through complex problems.
Rather than treating AI as a threat to assessment, some faculty are using it as a catalyst to revisit learning outcomes and evaluation methods.
Measuring Reasoning, Not Just Results
Many of the assessment approaches gaining traction share a common characteristic: they make student thinking more visible.
That may include project-based work, iterative assignments that document progress over time, opportunities for reflection or activities that require students to evaluate and critique AI-generated outputs. These approaches focus less on the final product alone and more on the process students use to arrive there.
The goal is not to eliminate AI from learning. Instead, it is to create learning experiences that require students to analyze, question, apply and explain.
In that sense, AI may be pushing higher education toward forms of assessment that more closely mirror real-world problem solving, where success often depends less on producing information and more on evaluating it, interpreting it and acting on it effectively.
As Dr. Ford notes, the opportunity is to create learning experiences where “the process matters as much as the product.”
Supporting Faculty Through the Transition
Assessment redesign requires time, support and experimentation. Not every instructor can overhaul an entire course overnight.
The encouraging news is that targeted support appears to make a difference. The findings show that faculty who participate in AI course redesign training deploy twice as many assessment approaches as those who do not.
That suggests that institutions do not need all the answers immediately. What they do need is to invest in helping faculty explore new approaches, share practices and develop confidence in redesigning learning experiences for an AI-enabled world.
The institutions seeing progress are not necessarily those with the strictest policies. They are often the ones creating space for faculty to experiment, learn from one another and rethink assessment with intention.
Looking Ahead
As another academic year begins, the conversation around AI is moving beyond whether these tools belong in higher education. Instead, a more important question is emerging: How do we ensure assessment continues to measure what matters most?
The answer may not lie in designing assignments that prevent students from using AI. Instead, it may require designing assessments that reveal how students think, reason, solve problems and apply knowledge, whether AI is part of the process or not. Faculty who are embracing that shift are already providing a glimpse of what the future of assessments may look like.
CTA: Explore the full Time for Class 2026 findings to learn how faculty, students and institutions are responding to AI’s growing impact on teaching and learning.
Written by:
