With no background in coding, Faith Maeba, a psychology major, was reluctant when her mother first suggested she enroll in classes on artificial intelligence.
But the senior at Virginia Commonwealth University began to see it differently as she looked into graduate psychology programs that explore human behavior in the workplace, which is quickly being upended by machine learning. Maeba, 21, is now pursuing a minor in AI.
“It’s giving me an edge and standing out,” she said.
Hiring has cooled for entry-level software developers — work increasingly done by AI agents — and college enrollment in computer and information science programs has been declining. Yet at campuses across the country, many professors are finding themselves busier than ever teaching students from a range of majors about artificial intelligence.
Colleges are responding to changes in student demand, but they also recognize that new graduates — regardless of their field — are facing questions about their AI skills from potential employers.
“We have to democratize it,” said Peter Stone, the chair of computer science at the University of Texas at Austin, who recently developed an introductory course on AI essentials for noncomputer science majors.
“In the same way that everybody needs some degree of math, reading and writing, I think everybody needs a degree of AI literacy,” he said.
That quote I strongly agree with, but I think you completely misunderstand what the professor means.
A huge problem in education right now is students using AI inappropriately, in many different ways. As an extension of the “digital literacy skills” education has been focusing on teaching in the last ~1-2 decades, educators are now teaching “AI literacy” skills, because students are using it wrong all the time. And it’s unacceptable both academically and professionally.
AI literacy skills are mostly about teaching students how bad AI is, what it can’t do, and why they should not rely on it. Like, understanding how an LLM works means that students will understand that they’re just fancy word predicting machines, not “intelligent” in any way. Students who understand this are much less likely to trust LLM output.
AI literacy skills are not teaching students how to vibe code faster.
‘AI literacy’ in the context of ‘don’t use it to cheat’ or ‘it may fabricate information or attributions’ should be an extension of institutional plagiarism and acceptable source policies; such policies can and should be discussed to ensure students understand them, including the implications of AI.
Framing it as being ‘literate’ in AI use, however, is a tacit encouragement (or at best acceptance) for such tools to be used.