Why Law Schools Are Banning Laptops to Fight AI Overreliance
When you picture a modern law school classroom, you probably imagine laptops open, students typing furiously as professors lecture, maybe even a few AI chat windows tucked discreetly in the corner of a screen. It’s the image of efficiency, of keeping up with the pace of technological change. But in a surprising twist, some institutions are hitting pause — not because they fear technology, but because they’re worried it’s undermining the very skills they’re trying to teach.
The University of Chicago Law School recently made headlines by announcing a laptop ban in certain first-year courses. The goal isn’t to reject progress, but to protect a kind of learning that feels increasingly rare: slow, deliberate, independent thinking. As generative AI tools become more adept at drafting legal memos, summarizing case law, and even mimicking judicial reasoning, educators are growing concerned that students might outsource their cognitive work before they’ve learned how to do it themselves. The response? A return to analog methods — handwritten notes, in-class essays without digital aids, and closed-book exams — not as a Luddite rejection of AI, but as a deliberate effort to build foundational skills before layering on technological shortcuts.
This shift reflects a broader tension in professional education. Fields like law, medicine, and engineering aren’t just about memorizing rules or procedures; they’re about developing judgment, spotting nuances, and constructing arguments from first principles. When a student can prompt an AI to generate a passable IRAC (Issue, Rule, Application, Conclusion) analysis in seconds, the temptation to skip the struggle of working through ambiguity is real. But that struggle — the false starts, the dead ends, the moments of insight that come only after wrestling with a problem — is where deep understanding is forged. By removing the option to rely on AI as a crutch during early learning, schools hope to ensure that when students do eventually use these tools, they do so with critical awareness, not blind dependence.
It’s worth noting that this isn’t a universal rejection of technology in the classroom. Many educators are actively exploring how to integrate AI responsibly — using it to generate practice problems, simulate client interviews, or provide instant feedback on writing samples. The laptop ban at Chicago, for instance, applies primarily to foundational courses where the focus is on cultivating legal reasoning from scratch. In advanced seminars or clinical settings, technology — including AI — may still have a role. The distinction lies in timing and intent: build the muscle first, then learn how to use the equipment.
Critics of such analog pushes argue that they’re fighting an inevitable tide. After all, practicing lawyers already use AI-powered tools for document review, legal research, and contract analysis. Shouldn’t schools prepare students for that reality? The counterargument is that fluency with tools requires understanding their limits. A chef who’s never chopped an onion by hand might rely too heavily on a food processor — and miss when the texture is wrong, or when a recipe calls for a finesse the machine can’t replicate. Similarly, a lawyer who’s never constructed an argument without algorithmic assistance might struggle to judge whether an AI-generated brief is genuinely persuasive or merely plausibly-sounding.
There’s also an equity dimension to consider. Not all students have equal access to the latest AI tools, or the technical savvy to use them effectively. By standardizing on analog methods in early stages, schools can level the playing field, ensuring that assessment reflects intellectual effort rather than access to premium subscriptions or prompt-engineering expertise. It’s a way of saying: we value what you can think, not just what you can ask a machine to produce.
Of course, going fully analog isn’t practical or desirable in every context. The real challenge for educators isn’t choosing between paper and pixels, but designing learning experiences that harness the strengths of both. Perhaps the future lies in phased integration: start with handwritten case briefs to develop analytical habits, then introduce AI as a research assistant in later years, always paired with rigorous critique of its outputs. Or maybe classrooms adopt “AI audits” — exercises where students must identify errors, biases, or omissions in machine-generated legal analysis, turning the tool into a subject of study rather than a silent partner.
What’s clear is that the rise of AI isn’t just changing how work gets done — it’s forcing a reckoning with what we value in learning itself. Are we training students to produce correct answers quickly, or to ask better questions? To replicate existing arguments, or to innovate within the framework of precedent? The analog push in some classrooms isn’t a rejection of the future; it’s an attempt to ensure that when students step into it, they bring more than just a prompt — they bring judgment, resilience, and the quiet confidence that comes from having figured things out on their own.
As AI continues to evolve, the most successful professionals won’t be those who use it the most, but those who understand it the deepest. And sometimes, the deepest understanding begins not with a click, but with the scratch of a pen on paper — a small, stubborn act of independence in an age of instant answers.