The first time I saw Michelangelo’s David in person, I wept. Never in my life have I witnessed a more beautiful piece of content.
I wasn’t the only one who felt that way. Museum patrons all around me were looking up from their phones to admire David as well. Despite it being legacy content, David was so scrollstoppingly captivating that it generated stronger engagement than TikTok and Instagram combined, despite those platforms offering fresher, more personally relevant content.
Imagine a pottery wheel that turns clay on an axis. A potter forms the sides and the shape goes all around. Karim Douieb made an interactive that shows how different curves look as a family of pots.
Potters don’t design bowls and vases, they design one curve. That single line, rotated around the axis, becomes the entire form.
In Line, you draw that curve. One side, shaped with a handful of control points, and the app grows a family of proportions around it: each variant keeping the radii and angles that make your line recognizably yours. Taller, wider, slender, grand: different vessels, same gesture.
Tags: curve, Karim Douïeb, pottery
You may have seen news reports lately about the ways in which AI is taking over what were once generally considered white collar duties. People are using it to write emails, to take notes, to compile presentations. Doctors are using it to record your symptoms, lawyers are using it to 'research' cases and real estate agents are using it for...everything.
Yet as more bosses begin to mandate AI's use in the office, and more professionals bring it ever more into their working lives (whether of their own free will, or because of company orders), have any of them ever stopped to wonder just what it is they do, exactly?
A white collar job does not generally involve manual tasks. It is, for the most part (and sorry if this sounds like an ad slogan), a job of the mind. From managers to HR, events to compliance, accountants to physicians, people are in those jobs and are getting paid for them because of their (supposed) ability to communicate with others, analyse things and interpret meaning from events. To draw upon their training, their experiences and their beliefs to do their work.
Let's say I'm an office manager or a HR rep, and my job is to settle a dispute between two workers. To settle that dispute is part of my job, it's why I am there. My skills at talking to people and conveying my thoughts and opinions via email are why I am being paid. If I open up ChatGPT and ask a machine how to do it--a machine that does not know how to do it, it only knows how to synthesise one million examples of this, because it is a machine--I am not doing that work anymore. Anyone could have asked the machine that question.
Or, if I'm a lawyer and my job is to prepare for a case, I am about to get paid a lot of money, because legal work isn't just hard, it requires a lot of training and has a lot of responsibility attached to it. If I start using ChatGPT to cut corners and research things for me, or even help me write things, why should I keep getting lawyer money if some kid off the street could have punched in the same prompts?
These people are hastening their own obsolescence, not because AI can do their work (it can't), but because by thinking it can they've shown themselves to be some of the dumbest people in the workforce, a collection of rubes who will throw decades of best practice out the window just because some Silicon Valley billionaire is jangling a shiny set of keys at them.
For all of the defence of AI from its boosters, cries that it's just a 'tool', it's anything but. We are years into this supposed revolution and AI is still hallucinating, making basic citation errors and failing to understand the nuance of a situation because it will never be able to. Robots might be able to do the work of a human arm, but the human brain is clearly a different story.
A combine harvester that does the work of ten farmhands is a tool. A nailgun that lets a builder put a frame up in a fraction of the time it would have taken with a hammer is a tool. Whatever the effect of these technologies on labour or capital in those contexts, it is indisputable that those tools can do the exact same work, only faster.
A machine that tries to do your job and fails (in sometimes spectacular, embarrassing and even dangerous ways) but which you keep using anyway isn't a tool. It's a scam, one you and your bosses have fallen for. In racing to make use of it in an office job you're not optimising workflow or not getting left behind, you're showing that you either cannot or are now simply choosing not to do your job anymore.
Letting a machine write your emails or a talk or spin up a PowerPoint presentation isn't cutting corners, those tasks are literally what you're being paid for. That work is the work.
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Paige Wyatt heard plenty from administrators about using AI to generate lesson plans at the school where she taught high school English.
Her students used AI to cheat so frequently she resorted to spending her own money on detection tools. Yet she never got help with what she really wanted: how to effectively catch and stop the cheaters, some of whom were very clearly just copying and pasting from Google’s AI summaries.
Teachers like Wyatt consistently identify cheating as a major concern in the AI era, and 70% of teens report using AI for schoolwork, a Common Sense Media survey published last month found. But that concern is getting little recognition from states. While at least 13 states have passed laws about AI in education, the laws most often direct state agencies and districts to create their own AI guidance, meaning lawmakers have frequently absolved themselves from making tough decisions.
Meanwhile, roughly three out of four states have published information to help guide AI use in education. But a Chalkbeat review found these documents tend to skirt the issue of cheating, offering little in the way of concrete steps to halt or deter students from offloading work to AI. Instead, they dedicate more real estate to AI literacy and ways teachers can use AI for efficiency.
All of that beating around the bush has left educators and districts to their own devices to come up with ways to deter and catch AI-enabled cheaters. It’s become an exhausting endeavor that hasn’t turned up any definitive solutions, educators and researchers said.
There’s no surefire way to detect AI-generated content. Nor is there an agreed-upon definition for cheating: Can students use AI to outline an essay but not write it? Can they use it to copy edit? Some states have crafted acceptable use policies which make some, but not all, of these distinctions.
Wyatt quit her teaching job last year. She didn’t leave solely due to AI, but its impact on her work was a factor.
“We really need real consequences,” she said. “We need workshops for teachers to detect cheating. We need workshops for kids.”
Most of the nation’s 10 largest school districts are developing or have developed more specific policies to try to prevent using generative AI to cheat. But it hasn’t always been easy: New York City delayed the rollout of its AI guidance for months amid fierce backlash, before announcing a new policy this week. Half of those districts explicitly discourage using AI detection tools.
Many of the official responses might underscore the urgency of the problem without helping to address it, which could only increase teachers’ anxiety.
“I think the situation teachers are in feels bad, and feels like they ought to have a solution now that they don’t have,” said Justin Reich, a professor at the Massachusetts Institute of Technology and the host of a podcast about how AI is shaping K-12 education.
Historically, state policymakers haven’t rushed to create laws governing new technology and education.
States didn’t really start to adopt computer science requirements until the 2010s. And while the iPhone launched a smartphone revolution in 2007, the first state restrictions on cell phone use in schools didn’t come until 16 years later, in 2023.
States may be moving faster when it comes to mainstream generative AI, which took off with the advent of ChatGPT in late 2022.
“Folks can’t just hope this will pass. This is a major change, and students are already using it,” said John King, former U.S. education secretary and current chancellor of the State University of New York system. “This isn’t tomorrow’s problem. This is today’s problem.”
The District of Columbia and at least 37 states have issued recommendations for public schools about AI. Their documents range from short webpages to Illinois’ 400-page behemoth. Many define different types of AI, stress that any AI use should still be led by humans, and link to outside resources — some of which have been developed with the support of big AI companies.
But they often devote just a page or a few paragraphs to academic integrity.The documents also seem light on actual guidance and don’t include many citations, Reich said. That’s because there’s little evidence to show how effective AI tools are in the classroom, or what policies would effectively stop cheating.
“If you just let this run amok in secondary schools, all kinds of learning activities will stop,” he said. “We do not know what to do. There’s no evidence-based policy. There’s no basis for development and professional development. Like we’re just making stuff up.”
More than a third of states urge schools not to rely solely on tools that promise to detect the use of AI, citing studies showing they are not effective and incorrectly flag genuine student writing as AI.
But many states then offer little else other than acceptable use policies. In California’s case, the state offers a list of questions for districts to mull when making policies around integrity in addition to an acceptable use policy.
In the absence of hard and fast rules, teachers have reported requiring students to complete more graded work in class, using pencil and paper.
Christopher Harris, the director of the school library system for the Genesee Valley BOCES, a regional district in western New York, wants educators to go further than that and reconsider how they measure learning. Writing may be less of a valid tool to assess if students are learning, now that AI “can suddenly write anything.”
He said students could submit an oral assignment, or teachers could focus on the learning process more than the product by asking students to explain how they respond to feedback when developing a project or presentation.
New York is one of the 13 states that hasn’t published AI resources for educators. Harris’ library system has been developing resources about AI for the state’s educators using grant money.
“I really wanted to help teachers understand they can’t control what kids do with AI,” he said. “The only control they have is their agency in developing assignments.”
Sara Brescia thought she had a no-brainer policy proposal for her fellow Manassas City Public Schools board members: adding AI misuse to the district’s code of student conduct.
The policy she proposed for the district in northern Virginia — a hotbed for the data centers AI relies on — would prohibit students from trying to pass off AI-generated content as their own. It specified that AI could only be used with a teacher’s permission, and required students to cite AI use. Students would be punished, up to and including with expulsion, for violating any of those provisions.
But other board members voted her idea down in late July. Lisa Stevens, another school board member who is also a teacher according to her LinkedIn page, worried the policy would add a burden onto teachers without giving them any tools to identify AI-generated work.
Brescia is skeptical about the value of those tools.
“We do not have a comprehensive approach,’’ she said. “We have the student code of conduct that says nothing.”
Wyatt said when she left the classroom, students were only becoming more fluent in methods to cheat. They were finding tools like QuillBot to disguise AI-generated writing and make it sound more like student writing.
She’s open to going back to the classroom, if she gets more administrative support to help detect the kind of AI misconduct that felt out of control when she left.
“As a community, we need a support network,” she said.
Lily Altavena is a national reporter at Chalkbeat. Contact Lily at laltavena@chalkbeat.org. Lily is on Instagram, Tik Tok, and Facebook.
Wendell Berry, who died at his home in Kentucky yesterday, was a rare combination of poet and farmer. That wasn’t always an unusual thing in the literary world, but the authenticity of agricultural and pastoral poetry has been in decline since the days of Hesiod. Even Virgil was a bit of a poser. But Berry was the real deal in every way.
That would set him apart in any age, but especially in our own time, when so many of us (writers included) are cut off from nature and the holistic ecosystems of agrarian life.
To mark his passing, I’m sharing this article from my archive on Berry’s nine rules for technology. It’s a simple list, but a profound one—and even more relevant today than when it was first published in 1987.
Back then, Wendell Berry was living on a farm in Kentucky. He did his writing with pen and paper, and his wife Tanya would create typewritten drafts of his manuscripts on a Royal standard typewriter purchased in 1956. That manual tool was, he insisted, “as good now as it was then.”
I’m sure he said the same thing about the implements he used to sow and reap. But friends told Berry he needed a computer. It would make it easier to write, they insisted.
In response, Berry came up with his list of nine reasons to embrace new technology. Let’s revisit them, one by one.
This is a very persuasive selling point for new technology. And for most of my life, tech companies worked hard to lower prices.
I still recall my parents scrimping and saving in order to buy a color television when I was seven years old. It cost almost $500—a huge amount in those days.
They probably should have waited. A few months later, RCA dropped prices to $399. Prices continued to drop in later years. You can buy a high tech TV today at Best Buy for less than what my parents paid in the 1960s.
Computers also got more affordable—at least until recently. I got my first computer (an Apple IIE) when I was in graduate school—it was an expensive gift from the Boston Consulting Group in exchange for accepting their job offer.
The list price back then was $1,400. I could never have afforded to buy it on my tight student budget.
But, over a period of many years, each subsequent computer I acquired was better and cheaper than my previous model. Alas, that happy trend has now ended.
When I buy a new computer now, I pay more. And the performance is not always better. I recently had to scrap a new desktop after only a few months, and go back to my previous model. The new computer didn’t work as well as my five-year-old one.
When did new tech stop getting cheaper and better?
I have a cranky answer, but an accurate one. It happened the day Steve Jobs died. Maybe not exactly on that date—but shortly afterwards.
Look at this chart of iPhone prices, adjusted for inflation, and you can see what I mean.
Now let’s go to the second reason to adopt new tech from Wendell Berry’s list.
This is another good reason to upgrade your setup. And tech did get smaller for many decades.
Guess who played a key role in that? Yes, Steve Jobs again. Because of his obsession with product design, we now carry a huge amount of advanced tech in our pocket.
Just consider this remarkable fact: Every device featured in this Radio Shack advertisement from 1991 has been replaced by your tiny phone.
But this, too, changed soon after Jobs died. (Are you noticing a pattern here?)
The thinnest iPhone ever was the iPhone 6 (2014)—at a slim 6.9mm. The company continued to launch ‘mini’ models for a few years, but stopped after iPhone 13.
Tech is now bulking up. It’s not just the devices—wait until you see those AI data centers. A single facility can spread over two kilometers.
This is the most obvious requirement for new tech. It needs to work better than old tech.
But Silicon Valley has totally abandoned this ideal. Every web interface I use has gotten worse over time—from search engines to social media to software to shopping apps.
Google is worse than ever. Social media is worse than ever. Amazon is worse than ever. Facebook is worse than ever. Everything I get from Microsoft is worse than ever.
So here, too, we see that new tech previously fulfilled Berry’s requirement—but stopped doing so around the time Steve Jobs died.
Here, again, we see an ominous reversal. With the rise of AI, tech companies now use up more energy than ever before. They are sucking the power grid dry in many places.
And it’s going to get worse—much worse.
What makes this especially revealing is the fact the public intensely dislikes AI—surveys make this absolutely clear. So tech companies are destroying the environment solely to increase their dominance and control—not to please you and me.
Now Berry is asking for something our technocracy has never delivered. And here we encounter the exact opposite of the AI situation described above. The hot new plan is to construct nuclear reactors on site. What could possibly go wrong?
There’s another striking contrast here. AI depends on huge investment from corporations, while consumers are mostly indifferent. Solar energy is the opposite: It’s supported by investment from consumers—who use it to heat their homes, water, etc.—while corporations are mostly indifferent.
What a sad state of affairs. Private citizens have more prudent approaches to tech than the tech companies themselves.
This, too, has changed during my lifetime. I once saw my father unscrew the back of our home TV set, and fix a malfunctioning part. Nowadays you can’t even open up those bad boys.
Tech providers create all sorts of obstacles to prevent repairs—unusual screws, arcane software, special tools, etc.
Consider the case of John Deere tractors, which wouldn’t start until a company-trained technician cleared out the error code. The company also refused to sell spare parts. Their practices got so abusive that politicians passed right-to-repair bills to protect farmers.
But the worst example happened during the COVID pandemic, when companies tried to prevent hospitals from fixing their malfunctioning ventilators. Manufacturers put software locks on this life-saving equipment to prevent repairs.
This represents a total failure on the part of the technocracy—and actual malfeasance by the executives who run these companies.
Finally I can give some tiny credit to our tech titans. They do offer home delivery—even if the product is made in a sweatshop far, far away.
This is a pipe dream. The tech product lifecycle is built on planned obsolescence, not simple repairs.
When your device or software stops working, you replace or upgrade—whether you want to or not.
In some instances, you aren’t even allowed to own, let alone fix, your tech—you just license or lease or subscribe. It’s almost like capitalism in Silicon Valley has turned into communism. You will own nothing, and will love it.
This may be the biggest tech failure of them all.
The leading tech companies have deliberately promoted dysfunctional apps that destroy lives. And they know it.
Leaked internal documents from TikTok show that they were aware that teens get addicted to their app in just 35 minutes. They built it that way.
Facebook knew that Instagram use leads to depression, anxiety, eating disorders, and other problems.
Spotify insiders have confirmed the company’s systematic plan to reduce royalties to musicians by manipulating passive listeners.
For more examples, see my list of 52 indicators that technological progress is reversing.
This is the new normal for tech: It deliberately makes things worse, not better.
Here’s the entire list of Wendell Berry’s criteria. If this were a report card, your tech leaders would all get failing grades.
The curious fact is that the most up-to-date and forward-looking thing is this whole article is Berry’s list from 1987. Nothing on it is obsolescent or inappropriate or dysfunctional or harmful.
I wish our tech companies could say the same for their work. Maybe they could learn a thing or two from our poet-farmer—or at least take a look at his list. We can only hope.
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.
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