Separate AI Literacy and Assessment Integrity

Two conversations that keep getting in each other’s way — and what to do about it

Two weeks ago, I wrote about the conflation of two discussions that happen constantly in faculty meetings and AI working groups:

Discussion 1: Whether and when to use AI — an ethical question.

Discussion 2: How to teach students to use AI better, so they protect their own cognition and creativity — a question of maturity and skill.

These two conversations talk past each other and produce stasis. Today I want to name a second conflation that creates the same problem.

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Here’s the situation on most campuses right now. A new College Board survey of more than 3,000 U.S. college faculty (February 2026) found that 74% of faculty report students using AI to write essays or papers, and 45% hold an overall negative view of AI in higher education. A separate survey from the American Association of Colleges & Universities and Elon University’s Imagining the Digital Future Center put an even starker number on the table: 95% of faculty believe AI will make students dangerously over-reliant on technology, and 78% say cheating on their campuses has increased since AI became widely available.

Faculty are scared, frustrated, and increasingly burnt out by a problem that keeps getting bigger.

But here’s the thing. The conversation meant to address that problem keeps splitting in two — and neither half can move until someone separates them.

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Two Problems. One Meeting. No Progress.

Teaching AI Literacy and Preserving Assessment Integrity are not the same problem.

Assessment integrity has deteriorated because of AI. Students can more easily cut corners on assignments, which destroys an educator’s ability to evaluate student thinking in any digital space. This is a real problem with real institutional stakes.

AI Literacy is a different project. It focuses not on preserving assessment integrity but on teaching students to be critical thinkers in the chat — to reflect, push back, and use AI in a way that builds their own cognition rather than replacing it. Researchers like Ethan Mollick at Wharton — author of Co-Intelligence and one of TIME’s Most Influential People in AI — have argued consistently that how students engage with AI determines whether the technology strengthens or atrophies their thinking. Marc Watkins, writing at Rhetorica, makes a parallel case: pedagogical approaches need to blend AI engagement with analog methods that protect human agency and judgment.

Both problems deserve serious attention. But they are not the same problem. And when they sit in the same meeting, neither gets solved.

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What Happens When You Try to Solve Both at Once

Some AI Literacy practitioners — myself included — have tried to put the two together. What if we taught AI literacy in the context of our assessments? Might that preserve assessment integrity while building the literacy we need? Two birds, one stone?

It is possible. My framework for comparative transcript analysis has succeeded in both changing student perception of AI use and providing educators with a viable window into student thinking in context. You can read more about its first application in the Foreword of the Field Guide to Effective AI Use.

But as I’ve sought to scale the mechanism, the conclusion has become clear: trying to accomplish both at the same time puts a massive cognitive burden on both the educator and the student. In Aimee Skidmore’s and my Elsevier chapter (due to publish later this year), we document what this looks like in practice — four weeks of sustained effort to run one coherent cycle. It works. But it’s not scalable, and it’s not realistic to ask of most faculty.

The EDUCAUSE 2025 AI Landscape Study confirms the structural problem: only 22% of institutions have an institution-wide AI strategy, and for 55% of campuses, AI policy is happening in fragmented pockets. The AAUP reported in 2025 that 71% of faculty say administrators overwhelmingly lead AI conversations with little meaningful faculty input — and only 20% of colleges and universities have published any formal AI policy at all.

The institutions that are spinning their wheels aren’t failing because their people are bad. They’re failing because they’re asking one committee to solve two fundamentally different problems at the same time.

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The Fix: Split the Problem

Here is what the separation looks like in practice.

Two Committees

Instead of one AI Committee or Working Group, higher ed and K-12 institutions should create two:

• An AI Literacy Working Group focused on what mature, critical AI use looks like — how students engage in the chat, how to build metacognitive habits, how to teach AI in its broader societal context.

• An Assessment Integrity Working Group focused on how to evaluate student thinking in an AI-saturated environment — portfolios, process-based assessment, project-based learning, conversation-as-artifact. These solutions do not need to involve AI at all.

The key insight: assessment integrity can be preserved or rebuilt without involving AI literacy at all. AI literacy can be built without navigating around AI cheating. These are separate tracks aiming at separate outcomes.

Two Sets of Experiments

For the AI Literacy Working Group: experiments that center on what it means to use AI in a way that builds the user. Teaching skills in the chat. Teaching students to place AI in its broader societal, economic, and political context. Training metacognition, not just prompting.

For the Assessment Integrity Working Group: experiments focused on portfolios, project-based learning, and process-based evaluation mechanisms. The EDUCAUSE ALTL framework (October 2024) and work from researchers like Laura Kristen Allen and Panayiota Kendeou at the ED-AI Lit Project provide strong scaffolding for the literacy side. Academic integrity scholars Tricia Bertram Gallant and David Rettinger address the integrity side directly in their 2025 book The Opposite of Cheating — the thesis being that academic integrity is fundamentally a teaching and learning issue, partially solvable through good pedagogy.

Think about what we’ve been doing instead. We’ve put two groups of educators — with genuinely different goals — in the same room, pointed them at the same deadline, and then wondered why progress is so slow.

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Why This Matters Now

The frustration simmering across campuses is real. Research published in the Journal of Academic Ethics argues that AI didn’t create the assessment integrity crisis — it exposed and accelerated a structural vulnerability that was already there: massified, standardized assessments designed for administrative convenience rather than pedagogical fidelity. AI made that vulnerability impossible to ignore.

The answer isn’t to bolt AI literacy onto the integrity crisis or vice versa. The answer is to give each problem the focused attention it deserves — separate working groups, separate experiments, separate success metrics.

I’m not under the illusion that this idea travels quickly. For faculty still in the anger, denial, or bargaining stages of the AI transition, it may not land yet. But for the educators already out front — already asking the harder questions — this distinction is useful. We’ve been splitting our focus in two and aiming it at a single goal, when the two tracks are actually trying to achieve something different.

The call to action? Name it. Separate it. Make progress.

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A note on how I’ve organized my own work along these lines: AI Literacy Partners (litpartners.ai) is my consultancy — that’s where I do AI literacy work with institutions. AI Friction Labs (aifrictionlabs.com) is a separate product built specifically to address the assessment integrity problem. Two different projects, two different websites, for exactly the reason this post argues. If the assessment integrity track is the one you want to follow closely, the AI Friction Labs newsletter is the place to do that — sign up at aiFL Newsletter Sign Up.