Universities across the country are investing in artificial intelligence literacy. Students are attending workshops, experimenting with generative AI tools and earning certificates meant to show that they are ready for an economy shaped by AI.

The problem is that we don’t know if these programs help students solve workplace problems, if they open doors to job opportunities, or both. We also don’t know if they give students from different institutions a fair chance to compete.

Higher education has an AI pilot program problem. Much of the evidence for “successful” programs still comes from single-region studies and relies on short-term measures such as test scores and self-reported data. Too often, they are described as successful before anyone can show whether students have actually used what they learned beyond the classroom.

Colleges should stop treating AI literacy as the finish line and start building clear pathways from learning to workplace application. Every AI literacy program should be designed with employers, tested across institutions and evaluated by what students can actually do afterward. Attendance, certificates and satisfaction are not enough. The goal must be readiness.

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AI literacy is necessary. Still, just knowing how to use a tool does not mean a student knows how to apply it responsibly to an unfamiliar workplace problem. A student may know how to ask a chatbot a question without knowing how to check the answer, protect private information, explain a recommendation or decide when the tool should not be used. Those abilities develop through repeated practice in real contexts. That is why colleges and universities should adopt shared measures of AI workforce readiness.

The stakes are especially high for students at public and less-resourced institutions. They may be introduced to AI tools without receiving enough practice, guidance or employer connections to turn that introduction into an opportunity.

Earlier this year, I founded an initiative to examine this challenge across two different settings. The program brought CUNY undergraduates and NYU Tandon graduate engineering students into the same hands-on series of workshops. Industry professionals showed students how AI is used in actual jobs, and students practiced using the tools for workplace tasks and explaining their decisions.

Our small, preliminary study found that the two groups reached similar levels of applied AI use. Participation emerged as the clearest factor in predicting that use. Among students who attended one or two workshops, regardless of their school, 21 percent created something with AI. Among those who attended six or more, 56 percent did. The study needs to be repeated elsewhere, but it did not detect a difference between the two institutional groups on this measure. The design of the program, along with the amount of meaningful practice students receive, may matter even more.

Related: OPINION: Schools cannot teach AI literacy without a way to measure it

A useful question is whether particular combinations of instruction, practice, professional guidance and real-world opportunity produce meaningful results, and which students benefit. Answering that question will require universities to move beyond isolated pilots.

First, to evaluate their AI literacy programs, colleges should measure changes in students’ judgment, problem-solving, professional communication and ability to create useful work with AI. They should also track whether students gain access to internships or employment.

Second, universities should study programs across different types of institutions. Findings from any one program cannot automatically be assumed to generalize across different institutional contexts.

Third, employers should become partners in pilot program design rather than occasional guest speakers. They can help define real problems, review student work and explain which abilities matter on the job. Students need contact with professionals who can show them what responsible AI use looks like inside an actual organization.

Encouragingly, federal workforce policy is beginning to move in this direction. In July, the U.S. Department of Labor awarded nearly $162 million through five agreements to expand Registered Apprenticeship programs. The funding uses performance-based incentives tied to results such as the hiring of new apprentices, keeping them in programs and helping them advance.

Jobs for the Future, for example, a national nonprofit, received $40 million to support Registered Apprenticeship growth in roles building and maintaining the critical infrastructure that sustains the artificial intelligence, semiconductor and nuclear energy industries.

But apprenticeships are not university courses: They pay wages, provide federally recognized credentials and place learning inside a job. While colleges cannot simply copy that model, they can work toward the same goal: training that leads to demonstrated skills, meaningful employer participation and measurable outcomes.

Since generative AI reached college campuses, universities have shown that students are interested in learning how to use it. The next phase should determine which programs actually help them use AI responsibly and carry those skills into the workplace. Future grants should require shared measures, cross-campus comparisons and follow-up after students leave, since no single campus can build this evidence alone.

Without that evidence, AI workforce readiness will remain a collection of promising campus stories. With it, higher education can build a system that works for students no matter where they enroll.

Ngoc Cindy Pham is an associate professor of marketing at Brooklyn College, CUNY, and a visiting research professor at NYU Tandon. She is also a Fulbright Specialist and founder of BRIDGE AI Lab.

Contact the opinion editor at opinion@hechingerreport.org.

This story about college and AI was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Hechinger’s weekly newsletter.

The post OPINION: Knowing how to use AI tools is not the same as knowing how to apply them responsibly. Colleges must do more appeared first on The Hechinger Report.

Worms, Alter Ego, MacJesus ProGold, Portal (1986), and many more

Most educators agree that there should be some guardrails regarding how students use AI in their independent work. Here, I argue for this guiding principle: Students should only use AI for things that they already know how to do well.

A story is told about Harold Alexander, British field marshal in command of the evacuation at Dunkirk. At the end of the workday he had the habit of taking any correspondence still in his “in” tray and simply putting it in the “out” tray. When his assistant once asked about it, he said “You’d be surprised how little of it comes back.”

That sums up my approach to the winds of educational change. When a new technology appears and people are asking themselves “what about my teaching should change?” my instinct for my own teaching is “change nothing,” partly because I suspect that the excitement is overblown, and partly because I’m happy to let more venturesome people make a change first, and learn from their experiences.

But that strategy can’t be applied in the case of AI. In 2023 I, like every other educator, realized that I had to respond to student access to large language models.

Administrators have had a few years to think through how teachers should change classroom practice to meet the new reality, and many districts have posted guidelines. The policies I have seen have much in common, and I think they are wrongheaded.

(Throughout this post I’m referring to students’ independent use of AI, not to educational software nor to teachers using AI in their work.)

There seems to be widespread agreement that there’s a problem, and even on how to characterize it. Learning is a product of engaging in mental work, and students may use AI to replace that mental work and hence, not to learn. The question is what to do about it.

Almost no one advocates for “no restrictions on independent AI use” nor for “No independent AI use by students. Period.” The overwhelming majority have taken the position that students should use AI, but there should be limits.

Mashable recently ran an article summarizing the response of the US’s largest school districts. Here are the one-sentence versions, as I read them:

New York City: students are allowed to use AI for basic “research, exploration, and creative projects.” There must be educator oversight.
Los Angeles: No AI use under age 13. Over 13, students can use AI to brainstorm and to edit their text, but students must produce text. Students must cite what AI contributed.
Chicago: Students can use AI for tasks like brainstorming and summarizing. They are encouraged to use AI as a study partner.

The policy of my home county (Albemarle, Virginia) is similar. There are published categories of AI support from which teachers may choose.

In short, teachers are given a good amount of leeway to allow students to use AI or to forbid it. There is, however, little or no guidance about how that choice should best be made.

These policies share a vibe. The goal is that students use AI as an associate in their work, but the student is still very much the lead. The second goal is that students be honest about exactly what AI has contributed.

This lead/associate orientation makes sense for life after graduation. Once a student is in the workforce, they will use AI to produce products: new ideas, reports, and so on. A guiding principle of “Use AI, but let me see your work” makes sense.

This is not, however, an optimal guiding principle for students. Students produce products, but the product is seldom the point. No one wants to read the papers they write, and the math problems they solve have no practical value. Teachers have them do these tasks because they provide practice in cognitive abilities that we think matter.

Writing is an especially interesting example. Writing a substantial paper requires integrating what you know across multiple sources, formulating a detailed argument, articulating your thoughts precisely, anticipating what your reader knows and can understand, and more.

For many educators, writing papers was the only tool we had to ensure that students engage these cognitive processes. AI took that tool from us, and we don’t know of a good replacement.

This loss was unprecedented. The closest analog might be math problems and calculators or Google Translate for foreign language. But in those cases a teacher could still remind students that they would not have these tools available during in-class assessments. I can’t give my students an in-class assessment that is cognitively comparable to writing a ten-page paper.

So the point of assignments is the mental processes required to complete them, and the point of the mental processes is learning. That seems to suggest a simple litmus test for the use of AI. Artificial Intelligence tools should not substitute for tasks wherein students would benefit from doing the mental work themselves.

So…do students learn when they brainstorm? Do they learn when they edit their work? Of course they do. This principle indicates that students should use AI only when doing the work themselves would yield little learning. This guideline echoes how most of us think about calculator use. Once you’ve mastered arithmetic operations, there’s no benefit to hand calculations when you’re solving an algebra problem. But if you’re still learning algebra, don’t use the algebraic functions. The slogan might be:

“Only use AI for what you already know how to do.”

I can see two objections.

First, can’t AI scaffold learning? Part of the learning process is doing things badly. Can’t AI offer pointers for improvement?

I see two problems with this idea. The first is that the novice is not going to be a very good partner to AI in learning. The novice doesn’t know enough to ask good questions. He will give the AI system a vague prompt about the goal of the exercise whereupon AI will sharpen it for the user. Essentially, it will chivvy the user toward the polished product, and the novice will simply accept the suggestions. (Remember, I’m talking about independent use of LLMs, not about a product custom-made to provide instruction. That’s a different matter.)

The second problem I see with AI-as-scaffold is motivational. Sure, students could learn from AI by asking it to critique their work, to generate counterarguments, and so on. And I’m sure some would, some of the time. But I think it’s asking a lot of students—or better, asking a lot of human nature—to expect them to take a much harder mental path for a project in a typical curriculum, even if it is in the interest of their own learning.

A second objection to the principle might be this: even after you reach competence, you still need practice; that will deepen and refine the skill and/or knowledge. For example, if a ninth-grader can write a good paragraph, is it now okay for AI to write paragraphs for the student?

If students are competent, a teacher should ask whether doing the task themselves is still part of what students are meant to learn or practice. When students are writing, editing, reasoning, problem solving, or brainstorming (for example), the mental process is usually the thing. You don’t want the help of AI because it replaces the mental work that contributes to learning.

The alternative is that AI is replacing a task that merely supports the actual mental work you want the student to do; the replaced task was soaking up the students time and energy and providing no benefit. That would be the college student hand-calculating long multiplication problems required for her engineering problem set.

For this reason I add the word “well” to the candidate principle: “Students should only use AI for things that they already know how to do well.”

Now of course there’s a judgment to be made here. A physics teacher might think “It’s great that AI will polish my students’ writing for their lab reports“ whereas someone else might argue “your students need to learn to write in your class too.” But of course this problem is not new. We’re just seeing the issue in a new guise.

We’ve all heard “They’re going to have to learn to use AI.” But I think that slogan applies to how students will use AI at home and in the workforce, where the product is the thing. For students, learning is the thing. That’s how I come to the principle, “Students should only use AI for things that they already know how to do well.”

"A register of the products that became the default in their trade." Early days. Largely US-focused.

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This article appeared in The New York Times, July 27, 2026

“Ms. Millman is a designer and brand strategist, the host of the podcast “Design Matters” and the chair of the master’s program in branding at the School of Visual Arts.

There is a stern warning to anyone who wants to study kabbalah, a mystical tradition within Judaism that seeks to uncover the hidden nature of God and the universe: Just wait. In traditional practices, prospective students are urged to delay their studies until they reach the age of 40, at which point it is assumed they will have developed the inner ballast to withstand what they might encounter.

We should consider doing something similar for artificial intelligence.

I understand how extreme this may sound, especially now that artificial intelligence has already entered seemingly every area of our lives. But the extremity is the point.

We have been so quick to treat access to new technology as an inevitability that we have failed to question whether every powerful tool should be placed in the hands of every developing mind the moment it becomes available.

I also see this as more than a theoretical issue. In the graduate program where I teach brand strategy, my students recently completed thesis projects that involve repositioning brands or concepts that have fallen out of pace with culture. At the beginning of the school year, we asked them to sign a document acknowledging that they understood the rules governing their use of artificial intelligence: They were allowed to use A.I. for research and for the visualization of ideas, but they were instructed not to use it to replace original thinking and writing.

We reminded them of these rules again at the beginning of the thesis process, because the thesis is not simply a deliverable or an exercise in presentation but also the place where their thinking is supposed to become visible. Because brand strategy is so dependent on the compelling communication of ideas, this is the part of the program in which students have to wrestle with language, evidence, doubt and the responsibility of making an argument they can confidently stand behind and defend.

Despite the agreement they signed and several serious reminders, we suspected that some students had used A.I. in place of their own original writing. When we asked, they squirmed and then admitted they had used A.I. to augment and synthesize their writing. I found their use of the word “augment” especially revealing because it allowed the act to appear smaller than it was; it made the intervention of A.I. in their work sound cosmetic and clerical, when what was actually being altered was the student’s relationship to struggle, authorship and accountability.

I don’t think my students are inherently deceitful or that they set out to behave with contempt for the work, the program or their faculty. While I was grateful that they readily admitted to the infraction, I wonder if they understood the gravity of the situation. This is what scares me most, as it is something far more ordinary: Students seem unable to resist the ease that large language models provide them to wrestle with and craft their ideas and are incapable of tolerating the discomfort of searching for language when language can be produced for them. They also seem unwilling to stay inside difficulty long enough for the difficulty to teach them something they will probably never learn any other way.

There is something druglike about using A.I. not because it produces pleasure but because it removes a particular kind of pain. A person who grows up with A.I. may never know the necessary agony of a blank page, the false start, the dull vocabulary and the private embarrassment of a first draft. A student who relies on A.I. too early will most likely never sit long enough with confusion to distinguish it from failure.

Confusion is a necessary state wherein people learn how to think for themselves. (Interesting turn of phrase, isn’t it? As though a person could think for anyone else.) Language is not merely how we express ourselves; it is one of the ways we become ourselves. We write in order to find out what we think, we revise in order to discover whether or not we believe it, and we speak, stumble, clarify, retract and try again and again in order to understand the space between ideas and truth.

The Jewish tradition treats kabbalah as knowledge that requires readiness. Wisdom doesn’t come instantly, and it certainly doesn’t arrive fully fluent. Wisdom is the slow education of the self, built by experience. Often we resist it, and more often, it requires us to revise what we once thought we knew. This can be humiliating, but that is why wisdom is also associated with growth. There is a reason we were told “No pain, no gain” and “I learned this the hard way” over and over as children.

I will admit that for a mature person, A.I. can be useful. A formed mind can argue or wrangle with the machine, reject its false polish and recognize when the language it constructs to redescribe the world has become too smooth or soulless. Someone who has lived with words long enough can sense when a sentence has not been earned, and someone who has already developed a voice can use the machine without allowing the machine to become the voice.

An unformed person may not know the difference, and that is also where danger lies. The obvious problem is cheating. But cheating is only the most visible and least interesting part of the problem. The more profound risk is that young people will use artificial intelligence to bypass the development of their interior lives and fail to hone their intelligence.

We do not wait for children to turn 16 to hand them car keys because cars are evil, and we do not insist that surgeons train for years because scalpels are immoral. We build thresholds around powerful things because power requires judgment, and judgment can’t be downloaded or installed. Before a person asks a machine to write for her, she should know what it feels like to write from the depths of her own uncertainty and identity. Before a person lets a machine imitate her voice, she should have endured the long, uneven, often humiliating process of acquiring one.

Of course, a rule like this is impossible as policy, and surely 40 is very late to adopt A.I. I know that artificial intelligence will not be held back by a cultural admonition, a syllabus clause, a signed agreement or even a teacher’s plea. There will be no collective vow of restraint, because the technology is already woven into the systems in which we are learning and working.

But impossibility does not make the argument meaningless. Sometimes a rule is valuable not because it can be enforced but because it tells us what a culture reveres. The old caution around kabbalah suggests that some knowledge is not improved by premature access. Kabbalah reveals that depth requires preparation, that mystery should not be consumed as content and that the human approaching any text matters as much as the text itself.

We need a similar caution now not because artificial intelligence is mystical or sacred but because it enters us through language and language is the medium through which we know ourselves.

Dawn was crowned fattest marmot by popular vote.

(Image credit: Eric Philips)