At UC Berkeley, failing grades in foundational math courses are rising (Deng, 2026). Faculty are reporting something they haven't seen before: students who can produce polished written work but can't execute basic calculations independently. The culprit, many say, is not a lack of ability. It's a habit — the habit of reaching for AI before attempting the work themselves.

This is happening at Berkeley. It's happening everywhere. And it raises a question that every institution building an AI strategy needs to answer: Are we designing for AI use, or are we designing for learning?

The Shortcut That Skips the Point

There's a well-established idea in cognitive science that effortful, even frustrating, mental work isn't an obstacle to learning — it's how learning actually happens. When a student retrieves information from memory, applies a concept to an unfamiliar problem, or works through confusion before finding an answer, their brain is doing exactly what it needs to do to build durable knowledge.

When a student pastes a homework problem into ChatGPT and submits the output, none of that happens. They've produced a result without acquiring a skill. Do it enough times, and the skill gap compounds (and a habit forms) which is precisely what Berkeley's math faculty are now watching in real time. The problem isn't AI. The problem is using AI to skip the part that matters.

What Most Platforms Get Wrong

The instinct in higher education has too often been to respond with detection: identify AI-generated work and penalize it. This is understandable, but it doesn't address the underlying dynamic. Students will find workarounds. The arms race between detection tools and AI capabilities is one no institution can win.

A more effective approach treats this as a learning design problem, not a compliance problem. That means building the structures that make productive engagement visible, and make bypassing it harder.

How ScholarStack Is Designed Differently

ScholarStack was built around a simple premise: learning is a process, not a product. The platform is designed to make that process legible to students, instructors, and institutions alike.

For students, the ScholarStack chat agent doesn't just provide answers. Before offering substantive help, it prompts students to articulate what they've already tried, where they're stuck, and what they think the answer might be. This isn't gatekeeping, it's what a good human coach already does, and it's backed by research: students who articulate their confusion before receiving assistance retain significantly more than those who receive unsolicited correct answers (Aleven & Koedinger, 2002). The act of self-explanation is itself a learning mechanism, distinct from, and arguably more actionable than, the general case for productive struggle.

"Make the productive engagement visible, and make bypassing it harder."

The platform also builds metacognitive scaffolding into the student workflow with prompts that ask students to predict, reflect, and self-assess at key moments. These aren't add-ons. They're drawn directly from decades of research on self-regulated learning, which consistently identifies metacognitive skill as the difference between students who learn strategically and those who remain dependent on external support (Zimmerman, 2002).

For faculty, the instructor dashboard makes engagement patterns visible in ways that grades alone cannot. A student who reads the material, attempts the problem, gets stuck, and then uses the AI agent to resolve a specific confusion looks very different from one who never opens the content at all. That distinction currently sits in a black box. ScholarStack opens it — giving instructors the data they need to intervene early and advise meaningfully.

For institutions, the administrative layer aggregates learning engagement data across departments, providing the kind of evidence that accreditation bodies increasingly require as they begin asking how institutions are ensuring learning outcomes in an AI-rich environment.

The Real Question

The goal was never to keep AI out of education. AI, used well, will be one of the most powerful learning supports ever developed, available at any hour, infinitely patient, endlessly adaptable.

The goal is to make sure students are learning, not just producing. That requires intentional design at every layer: the student experience, the instructor tools, and the institutional infrastructure.

That's what ScholarStack was made for.

Stay tuned for future posts about important topics on AI in education. Thanks for reading. 

– David Miller & Gladys Mercier