Are you ‘AI literate’? Schools and jobs are insisting on it—and now it’s EU law

Even tech giant Apple couldn’t prevent its artificial intelligence from making things up. Last month, the company suspended its AI-powered news alert feature after it falsely claimed a murder suspect had shot himself, one of several fabricated headlines that appeared under trusted news organizations’ logos. The embarrassing pullback came despite Apple’s vast resources and technical expertise.

Most users probably weren’t fooled by the more obvious errors, but the incident highlights a growing challenge. Companies are racing to integrate AI into everything from medical advice to legal documents to financial services, often prioritizing speed over safety. Many of these applications push the technology beyond its current capabilities, creating risks that aren’t always obvious to users.

“The models are not failing,” says Maria De-Arteaga, an assistant professor at the University of Texas at Austin McCombs School of Business. “We’re deploying the models for things that they’re not fit for purpose.”

As the technology becomes more embedded in daily life, researchers and educators face two distinct hurdles: teaching people to use these tools responsibly rather than over-relying on them while also convincing AI skeptics to learn enough about the technology to be informed citizens, even if they choose not to use it.

The goal isn’t simply to try to “fix” the AI, but to learn its shortcomings and develop the skills to use it wisely. It’s reminiscent of how early internet users had to learn to navigate online information, eventually understanding that while Wikipedia might be a good starting point for research, it shouldn’t be cited as a primary source. Just as digital literacy became essential for participating in modern democracy, AI literacy is becoming fundamental to understanding and shaping our future.

At the heart of these AI mishaps are the hallucinations and distortions that lead AI models to generate false information with seeming confidence. The problem is pervasive: In one 2024 study, chatbots got basic academic citations wrong between 30% and 90% of the time, mangling paper titles, author names, and publication dates.

While tech companies promise these hallucinations can be tamed through better engineering, De-Arteaga says researchers are finding that they may be fundamental to how the technology works. She points to a paper from OpenAI—the same company that partnered with Apple for news summarization—which concluded that “well-calibrated” language models must hallucinate as part of their creative process. If they were constrained to only produce factual information, they would cease to function effectively.

“From a mathematical and technical standpoint, this is what the models are designed to do,” De-Arteaga says.

Teaching literacy

As researchers acknowledge that AI hallucinations are inevitable and humans naturally tend to put too much trust in machines, educators and employers are stepping in to teach people how to use these tools responsibly. California recently passed a law requiring AI literacy to be incorporated into K-12 curricula starting this fall. And the European Union’s AI Act, which went into effect on February 5, requires organizations that use AI in their products to implement AI literacy programs.

“AI literacy is incredibly important right now, especially as we’re trying to figure out what are the policies, what are the boundaries, what do we want to accept as the new normal,” says Victor Lee, an associate professor in the Graduate School of Education at Stanford University. “Right now, people who know more speak really confidently and are able to direct things, and there needs to be more societal consensus.”

Lee sees parallels to how society adapted to previous technologies. “Think about calculators—to this day, there are still divides about when to use a calculator in K-12, how much you should know versus how much the calculator should be the source of things,” he says. “With AI, we’re having that same conversation often with writing as the example.”

Under California’s new law, AI literacy education must include understanding how AI systems are developed and trained, their potential impacts on privacy and security, and the social and ethical implications of AI use. The EU goes further, requiring companies that produce AI products to train applicable staff to have the “skills, knowledge and understanding that allow providers, deployers and affected persons . . . to make an informed deployment of AI systems, as well as to gain awareness about the opportunities and risks of AI and possible harm it can cause.” Both frameworks emphasize that AI literacy isn’t just technical knowledge but about developing critical thinking skills to evaluate AI’s appropriate use in different contexts.

Amid a marketing onslaught by Big Tech companies, the challenge facing educators is complex. Recent research published in the Journal of Marketing shows that people with less understanding of AI are actually more likely to embrace the technology, viewing it as almost magical. The researchers say this “lower literacy-higher receptivity” link suggests “that companies may benefit from shifting their marketing efforts and product development towards consumers with lower AI literacy.”

The goal isn’t to dampen openness to new technology, educators say, but to combine it with critical thinking skills that help people understand both AI’s potential and its limitations. That’s especially important for people who tend to lack access to the technology, or who are simply skeptical or fearful about AI.

For Lee, successful AI literacy requires seeing through the magic. “The anxiety and uncertainty feeds a lot of the skepticism and doubt or non-willingness to even try AI,” he says. “Seeing that AI is actually a bunch of different things, and not a sentient, talking computer, and that it’s not even really talking, but just spitting out patterns that are appropriate, is part of what AI literacy would help to instill.”

At the City University of New York, Luke Waltzer, director of the Teaching and Learning Center at the school’s Graduate Center, is leading a project to help faculty develop approaches for teaching AI literacy within their disciplines.

“Nothing about their adoption or their integration into our ways of thinking is inevitable,” Waltzer says. “Students need to understand that these tools have a material basis—they’re made by men and women, they have labor implications, they have an ecological impact.”

The project, backed by a $1 million grant from Google, will work with 75 professors over three years to develop teaching methods that examine AI’s implications across different fields. Materials and tools developed through the project will be distributed publicly so other educators can benefit from CUNY’s work.

“We’ve seen the hype cycles around massively open online courses that were going to transform education,” Waltzer says. “Generative AI is distinct from some of those trends, but there’s definitely a lot of hype. Three years lets things settle. We will be able to see the future more clearly.”

Such initiatives are spreading rapidly across higher education. The University of Florida aims to integrate AI into every undergraduate major and graduate program. Barnard College has created a “pyramid” approach that gradually builds students’ AI literacy from basic understanding to advanced applications. At Colby College, a private liberal arts college in Maine, students are beefing up their literacy with the use of a custom portal that lets them test and compare different chatbots. Around 100 universities and community colleges have launched AI credentials, according to research from the Center for Security and Emerging Technology, with degree conferrals in AI-related fields increasing 120% since 2011.

Beyond the classroom

For most people, learning to navigate AI means sorting through corporate marketing claims with little guidance. Unlike students who will soon have formal AI education, adults must figure out on their own when to trust these increasingly prevalent tools—and when they’re being oversold by companies eager to recoup massive AI investments. This self-directed learning is happening quickly: LinkedIn found that workers are adding AI literacy skills such as prompt engineering and proficiencies with tools like ChatGPT at nearly five times the rate of other professional skills.

As universities and lawmakers try to keep up, tech companies are offering their own classes and certifications. Nvidia recently announced a partnership with California to train 100,000 students, educators, and workers in AI, while companies like Google and Amazon Web Services offer their own AI certification programs. Intel aims to train 30 million people in AI skills by 2030. In addition to free online AI skills courses offered by institutions like Harvard University and the University of Pennsylvania, people can also learn AI basics from companies like IBM, Microsoft, and Google.

“AI literacy is like digital literacy—it’s a thing,” De-Arteaga says. “But who should teach it? Meta and Google would love to be teaching you their view of AI.”

Instead of relying on companies with a vested interest in selling you on AI’s utility, Hare suggests starting with AI tools in areas where you have expertise, so you can recognize both their utility and limitations. A programmer might use AI to help write code more efficiently while being able to spot bugs and security issues that a novice would miss. The key is combining hands-on experience with guidance from trusted third parties who can provide unbiased information about AI’s capabilities, particularly in high-stakes areas like healthcare, finance, and defense.

“AI literacy isn’t just about how a model works or how to create a dataset,” she says. “It’s about understanding where AI fits in society. Everyone—from kids to retirees—has a stake in this conversation, and we need to capture all those perspectives.”