When it comes to Learning, Struggle is the Process

Summary: Cognitive outsourcing is exactly what it sounds like: letting AI do your thinking for you. A Georgetown University trial showed the cost: med students who used AI performed well on exams, but the gains vanished within a week (Kalam et al., 2025). The science is old news — the AI influence is new. Scholar Stack logs AI use inside the course, so good practice becomes the default and struggle stays part of the process.

A student opens an AI tool, gets a polished essay in minutes, submits the assignment, and walks away feeling productive. They may even do well on an exam. But ask them to explain the same concept three months later and you get…crickets. 

That scenario, described in a recent Inside Higher Ed column (Brooks & Elwesmi, 2026) is not hypothetical. It is happening across campuses right now, at scale, and most institutions have not figured out what to do about it. The answer is not to ban AI from the classroom. The answer is to teach students how to use AI properly, and to give faculty the tools to make that teaching stick. 

We’ve Known This Problem for 140 Years

Learning science has a term for what happens when students skip the struggle: cognitive outsourcing. It shows up when a student asks AI to brainstorm before they contribute a single idea of their own, or pastes a paper into a chat window and accepts the summary without reading the original, or submits AI-generated code without even trying to understand how it works. The brain is bypassed. The student gets an output. They do not get an education. 

This matters because the struggle is not incidental to learning — it is the mechanism of learning. A randomized controlled trial at Georgetown University (Kalam et al., 2025) found that medical students who used ChatGPT during study sessions outperformed peers on immediate assessments, but the advantage had completely disappeared one week later. AI did not help them learn. It helped them perform in the moment. There is a significant difference.

This is the same mechanism behind why productive struggle works in AI-native instruction design. Elizabeth and Robert Bjork's research on "desirable difficulties" demonstrates that conditions which slow down initial performance, such as retrieval practice, spaced repetition, and interleaved problem sets, actually produce stronger, more durable learning over time (Bjork & Bjork, 2011). The friction is the point. Neuroscientists have a term for this biological process: synaptic plasticity. The brain literally rewires itself when it works through difficult material and arrives at understanding. That rewiring is what makes knowledge durable and transferable.  

"...struggle is not a flaw in the learning process. It is the process"

AI makes it dangerously easy to skip these kinds of learning reinforcements. Surface learning (Dolmans et al., 2016), the kind AI enables by default, is associated with forgetting 50 to 70 percent of new material within 24 hours. This phenomenon is known as the “forgetting curve”, discovered by 19th-century German psychologist Hermann Ebbinghaus in a pioneering experimental study of memory in 1885 (Murre & Dros, 2015). 

Good Learning Habits are Teachable

One educator in the Inside Higher Ed piece, Jacob Brooks, describes now he builds AI deliberately into his undergraduate physics courses. In a research and writing sequence, first-year students use AI to generate literature summaries, then go back to the original papers and compare. They find what is missing: methodological caveats, data limitations, entire arguments that the summary flattened or dropped. The most generative ideas for their own research often emerge from the gaps they’ve identified.

Brooks’ three expectations are clear: students must document how they used AI, including submitting conversation transcripts; they must understand and control whatever the AI produces rather than treating it as a black box; and they must never accept a first response without checking and refining it.

These are teachable habits. But they require deliberate course design and visibility into what students are actually doing. That last piece is where most institutions and instructors are flying blind.

A Pedagogical Responsibility

When students use off-the-shelf AI tools with no transparent connection to the course, faculty cannot see how those tools are being used. There are no transcripts, no usage patterns, no way to distinguish a student who understood the material from one who outsourced the thinking.

ScholarStack addresses this issue directly. All AI assistance is integrated into the learning environment, connected to course materials, and logged, so faculty can see engagement patterns and institutions can gather the kind of documented evidence that accreditation responsibilities now require. AI use becomes a visible, structured part of the learning process, not a private transaction that leaves no trace. ScholarStack is designed to make using - and learning - good AI practice the default, not the exception.

As a by-product of using AI to learn, students are also learning how to use AI. Teaching students to use AI well has become a pedagogical responsibility. Having the tools to support that kind of teaching is an institutional one. 

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

– David Miller & Gladys Mercier