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Nicole Weber recently published an interesting OLC Insights article, Do Asynchronous Courses Need Synchronous Sessions to Succeed?. She pushes back on the assertion that fully asynchronous courses need synchronous opportunities to truly support student success, drawing on her experience with asynchronous graduate programs and an institutional study of student, faculty, and administrator perceptions.

On one hand, I appreciate the discussion. I've argued for years that we should stop treating synchronous and asynchronous as binary choices, and Weber acknowledges that synchronous activities can provide meaningful benefits. She also makes important points about preserving flexibility, avoiding mandatory sessions that create barriers, and respecting students' circumstances.

On the other hand, I think her article illustrates a broader problem with instructional design assumptions, one that will become increasingly important as AI changes the nature of learning experiences. It is the assumption of a priori design as ultimate virtue.

The opportunity to combine synchronous elements with asynchronous online courses is something I've been tracking since at least 2021. In 2022, I highlighted Wiley research showing that 79% of online learners were willing to participate in some synchronous sessions despite a strong preference for asynchronous formats. The 2024 Risepoint survey showed a similar pattern: 76% preferred asynchronous formats, but 74% were willing to participate in live sessions. Student preference for asynchronous courses does not mean students want exclusively asynchronous experiences.

At OLC Innovate 2022, Joe Moreau and I discussed the need to avoid romanticizing pre-pandemic online education. One of my central arguments was that student engagement had long been an Achilles' heel of online education and that the pandemic had exposed opportunities to improve it.

Later that year, during my Digital Education Summit keynote, I suggested enabling students to organize their own synchronous group discussions, based on their schedules and needs. As I described the instructor's role:

The point wasn't simply to add more Zoom lectures. It was to provide opportunities for interaction and give space for students to decide how to use them.

This brings me back to Weber's article and one of her five design principles in particular.

She also argues that synchronous activities should align with learning outcomes and justify the costs imposed on students. These are reasonable considerations, particularly for required activities. But there's an underlying assumption that deserves scrutiny: How can designers know a priori which interactions will provide value, or even what that value might be?

This is where I see a form of design hubris, not just in Weber's argument but across much of the instructional design community. We assume that we can identify learning objectives, design the appropriate experiences, and specify how students should interact with content, instructors, and one another. We plan the learning experience in advance and expect students to follow the plan.

ADDIE is a prime example. The familiar Analyze–Design–Develop–Implement–Evaluate framework helped organize the instructional design profession, but its structure also encourages an a priori mindset. Analyze the problem, design and develop the solution, implement it, and then evaluate. Even with iterative variations, the fundamental orientation is toward specifying learning experiences before students encounter them.

The problem isn't simply how ADDIE gets implemented. Its structure makes overplanning appear virtuous. Too often, the process itself becomes a proxy for quality: objectives mapped, activities aligned, templates completed, boxes checked. Going through the motions of instructional design is not evidence that learning occurred.

I'm not arguing against intentional design. I'm arguing against the assumption that designers can—or should—anticipate every meaningful learning interaction.

Consider those student-organized synchronous discussions. Perhaps students work through a difficult concept, discover unexpected applications, or develop peer-support relationships that continue throughout a program. The instructor may not have anticipated these outcomes, but that doesn't make them less valuable.

The quality of design that we overemphasize so often is quite different than quality of the resulting learning experience. Sometimes good design means creating the conditions for learning without prescribing everything that happens.

The issue extends well beyond synchronous learning.

Much of the earlier generation of adaptive courseware assumed that designers could anticipate learning needs, develop appropriate interventions, and create predetermined pathways for students. Algorithms might select the pathway, but the possible experiences remained largely bounded by what had been designed a priori.

Generative AI changes that equation. These probabilistic systems can respond to unexpected questions, support exploration, and navigate learning needs that designers could never fully enumerate beforehand. They aren't inherently better at teaching, and their unpredictability introduces new risks, but they challenge the assumption that we must specify all useful learning interactions in advance.

This requires a different approach to instructional design, and particularly to assessment of learning.

Giving learners more agency does not mean relaxing academic standards. Quite the opposite. We should be less prescriptive about how students learn and far more demanding about demonstrating what they have learned.

Learning objectives and disciplinary standards remain essential. Instructors and designers need to focus much more attention on meaningful assessments that demonstrate actual understanding, reasoning, and the ability to apply knowledge. Not just completing activities, producing polished assignments, or passing quizzes that can increasingly be gamed with AI. We need credible evidence of mastery, including authentic demonstrations of learning, and the willingness to intervene when students are not meeting standards.

This is not a return to social constructivist relativism, where learners simply determine what counts as knowledge. Students should have more control over their learning experiences, but that doesn't mean they determine the academic standards. Instructors remain responsible for setting those standards, assessing achievement, and improving courses based on evidence of what actually works.

Less control over the learning path should come with more rigor in assessing learning outcomes. That is a more demanding role for instructional designers and instructors, not a diminished one.

I appreciate Weber's contribution and the broader OLC discussion. It is encouraging to see serious attention to combining synchronous and asynchronous learning while preserving flexibility. But I would encourage the instructional design community to go further and challenge some of the assumptions underlying its traditional approaches.

AI is changing what is possible, and a priori design models that made sense when learning experiences had to be carefully scripted are increasingly limiting. We need to become more comfortable with emergent learning experiences, student agency, and adapting based on what students actually do and learn.

The opportunity is not to abandon instructional design but to rethink its emphasis. Less time attempting to control every learning interaction, more attention to enabling meaningful experiences, and substantially more rigor in determining whether students have actually learned.

That's a version of learner-centered education worth pursuing.

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