Key Takeaways
- AI is already displacing work: a Stanford study shows a nearly 20 % drop in employment for young workers in AI‑exposed jobs such as programming and customer service.
- Anxiety about AI mirrors math‑anxiety research: the fear spikes not during the task itself but while anticipating it, suggesting that practice can alleviate the dread.
- Many institutions react with bans—e.g., the University of Chicago’s social‑science faculty banning most technology, UC Berkeley Law prohibiting AI use in all stages of written work, and the University of Chicago Law School removing screens from first‑year classrooms.
- Dartmouth and peer schools demonstrate a proactive alternative: appointing critical‑AI librarians, requiring AI‑based projects in business and medicine curricula, and letting students build and stress‑test their own models.
- The goal is to teach students what AI cannot do—asking the right questions, exercising judgment, navigating ethics, and taking responsibility—while giving them hands‑on, ethical practice with the tools.
- Without such guided practice, colleges risk ceding the shaping of AI’s use to the private sector and becoming irrelevant in an AI‑defined future.
The Growing Anxiety About AI on Campus
Students arriving this semester carry “a great deal of anxiety about AI and what it will mean for their lives after college.” Their worry is grounded in data: “New research from Stanford shows that, even though the much‑hyped AI job apocalypse has yet to materialize, the technology has already caused a nearly 20 percent drop in employment for young workers in AI-exposed occupations, such as computer programming and customer‑service work.” This statistic reveals that the labor‑market impact is already palpable, even if the full‑scale disruption remains forthcoming.
Why the Fear Feels Familiar
As a cognitive scientist who has studied how fear affects learning, the author notes that AI‑related unease “is also curable.” Citing earlier work with graduate student Ian Lyons, they explain that when math‑anxious participants had their brains scanned, “it wasn’t the test itself that sent people’s brains into a panic. Rather, the neural signals associated with threat detection and pain were most acute when people were merely anticipating the test.” The parallel is clear: the dread of AI stems largely from anticipation rather than direct interaction, suggesting that exposure and practice can mitigate the anxiety.
Current Institutional Reactions: Bans and Restrictions
Rather than confronting the anxiety head‑on, many colleges are opting for prohibitive measures. The University of Chicago’s social‑science faculty “have prohibited most technology and banned the use of AI tools from their core classes.” Law schools are similarly cautious: “In May, the UC Berkeley School of Law announced that students are prohibited from using AI in ‘conceptualizing, outlining, drafting, revising, translating, or editing any work submitted for credit.’” The University of Chicago Law School went further, deciding “to ban all screens from first‑year classrooms entirely.” These moves reflect a desire to preserve academic integrity but also reveal a reluctance to engage with AI constructively.
Why Blanket Bans Fall Short
While such bans may seem sensible amid cheating concerns, the author argues they are insufficient: “draconian mandates and screenless classes can’t be the only ways that colleges deal with AI.” Prohibition denies students the opportunity to learn how to work ethically and effectively with the technology, leaving the shaping of AI’s role to the private sector. If universities “opt for prohibition over practice, we will squander the chance to help shape how this technology gets used in the world—ensuring that the private sector will do this for us instead.”
A Practice‑Based Cure: Lessons from Math Anxiety
Drawing on the math‑anxiety study, the author proposes a solution rooted in deliberate practice: “When we supply students with the space to thoughtfully explore and engage with these tools, we can help ensure that they graduate into an AI-enabled workforce with more confidence and competence than fear, and with an appreciation for how to use AI tools ethically.” Just as repeated problem‑solving reduces math anxiety, guided interaction with AI can transform apprehension into skillful utilization.
Dartmouth’s Model: Teaching What AI Cannot Do
At Dartmouth, where the author serves as president, the mission is clarified: “we believe it’s our job to teach students how to do what AI can’t: decide which questions to ask, exercise sound judgment, navigate ethical complexity, reckon with uncertainty, and take responsibility for their decisions.” To operationalize this vision, Dartmouth has taken several concrete steps. It employs a critical AI librarian who helps students and faculty interrogate models, spot biases, and identify unsupported claims. At the DALI Lab, students “design and create technological products… [and] can now build and stress-test their own AI models, which elevates their understanding of the risks and rewards at play.” The ambition is for every undergraduate to complete a team‑based AI project linked to their coursework before graduation.
Specialized Training Across Disciplines
The practice‑oriented approach extends to professional schools. Dartmouth’s Tuck School of Business now requires first‑year students to use AI to build and test solutions for clients in their First‑Year Project; students who entered without AI expertise reported greater confidence by year’s end. In the Geisel School of Medicine, faculty created an AI Patient Actor that provides real‑time feedback on medical students’ communication and interpersonal skills, a tool now adopted by over 100 medical institutions worldwide. These examples illustrate how AI can be woven into discipline‑specific training while reinforcing human‑centric competencies.
The Broader Labor Market Imperative
The urgency is underscored by market data: “Globally, jobs that require advanced AI skills are growing eight times faster than the job market overall, according to an analysis from the professional-services network PwC.” Simultaneously, “entry‑level roles in AI‑exposed fields are more than seven times likelier than other entry‑level jobs to require the human skills once reserved for senior executives, including discernment, creativity, and leadership.” Thus, graduates must marry critical thinking with technical fluency to remain competitive.
No One‑Size‑Fits‑All, But Clear Intent Is Essential
The author acknowledges that pedagogy will vary: “One professor may decide that students should write their essays in a blue book in class, while another may ask them to use an AI tool and audit every step of this work.” Both approaches are valid provided faculty are clear about their ambitions and standards. Conversely, outright bans or vague restrictions “afford no opportunities for students to better understand how to work ethically and effectively with AI.”
Historical Responsibility and Future Relevance
Dartmouth’s legacy as the birthplace of AI—where “pioneering computer scientists launched the field of artificial intelligence on Dartmouth’s campus seventy years ago”—imbues the institution with a special obligation. If colleges sit out the AI revolution, they “abdicate a sacred commitment that has guided American higher education for centuries: to prepare the next generation of leaders to navigate an ever‑changing, always complicated, and frequently perilous world.” In a future defined by AI, universities that fail to produce graduates capable of using the technology productively risk irrelevance.
Conclusion: Guided Practice Over Prohibition
The path forward lies not in shielding students from AI but in giving them structured, reflective opportunities to engage with it. By teaching what AI cannot replace—judgment, ethics, responsibility—and providing hands‑on, ethically framed practice, institutions can transform anxiety into confidence. Such an approach ensures that graduates are not merely passive recipients of AI‑driven change but active shapers of a future where technology serves human purpose.
https://www.theatlantic.com/ideas/2026/09/universities-prohibiting-ai-classroom/688509/