University of Virginia teaches AI hands-on, not in theory, and shows a path forward for higher education
Artificial intelligence in the classroom is no longer a distant or futuristic topic. While many American universities are still trying to figure out how to respond to the rapid growth of AI tools, the University of Virginia decided to get ahead of the curve with a different approach: instead of isolated lectures and workshops, the institution built a complete framework to teach AI for real, with real projects applied in real courses.
The result is the AI Literacy and Action Lab, an initiative developed through a partnership between UVA’s College of Arts and Sciences and the university library. The goal is clear: train people who don’t just use AI, but who deeply understand what they’re doing when they use it. And the market numbers show this urgency makes perfect sense.
According to a recent Handshake report, 85% of this year’s graduates already use AI tools — a jump of 31 percentage points in just two years. More than a third of them use these tools every single day. On the job market side, over 10% of internship listings on the platform already mention AI-related skills, and the percentage of full-time job postings that reference AI nearly doubled compared to the previous year, reaching 4.2%.
Another survey, this one from EAB, found that 42% of students planning to enter college say AI will influence their career choice. And 10% of them have already changed their intended major because of the technology.
The question hanging in the air is: are universities preparing their students for this landscape, or are they still studying the problem while it evolves? UVA seems to have an answer for that. 👇
What makes the AI Literacy and Action Lab different
Most AI education initiatives within universities follow a similar playbook: a seminar here, a workshop there, maybe an elective course for anyone who’s interested. It works for raising awareness, but it rarely produces someone capable of making real decisions about when, how, and why to use AI in complex everyday professional situations.
The AI Literacy and Action Lab breaks that pattern precisely because it was built from the inside out — starting from the real needs of courses, faculty, and students themselves, rather than as a one-size-fits-all solution applied generically across the board.
Leo Lo, university librarian and dean of libraries at UVA, is the mind behind the framework that supports the lab. He developed a model based on five core competencies:
- Technical knowledge of how AI tools work
- Ethical awareness of the implications of using these technologies
- Critical thinking to evaluate outputs and identify limitations
- Practical skills to apply AI in real-world contexts
- Understanding of the social impact of artificial intelligence on society
The lab’s structure works by integrating hands-on learning directly into the university’s existing curriculum. This means students in humanities, social sciences, arts, and other fields that have historically been left out of tech conversations now have direct contact with AI tools within their own courses, through projects designed for each discipline’s specific context.
In practice, the program will be delivered through faculty-led pilot projects, a one-credit introductory seminar, a three-course sequence of one credit each focused on AI, and an incubation pathway for projects that extend beyond a single semester.
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As Leo Lo explained, the philosophy behind the initiative is straightforward: people learn best when they’re working on something that matters to them — a problem they want to solve, a question they want to answer. Instead of sitting through a webinar or a lecture, the idea is to learn by doing.
Why the library is at the center of everything
One decision that might seem unusual at first glance is the fact that the lab is anchored in the university library, not in a computer science or engineering department. But this choice was absolutely intentional.
Christa Acampora, dean of the College of Arts and Sciences at UVA, explained that librarians have always been at the forefront of access to information and knowledge. They were among the first professionals within universities to understand the uses of the internet and its impact on research — not just studying the technology, but actually using it.
For Acampora, the library is a natural space for this kind of work because it exists to serve all students and faculty, regardless of discipline. And that makes librarians ideal partners for an initiative that needs to be cross-cutting and accessible.
UVA isn’t alone in this approach. Other American institutions, like Bryn Mawr College, are also transforming their libraries into full-on AI sandboxes — safe spaces for experimentation and ethical use. There, librarians facilitate workshops and one-on-one consultations with faculty and students, focusing on digital literacy and practical classroom applications.
American university libraries have historically played a central role in curating and providing critical access to information, and bringing that tradition into the AI context makes a lot of sense. UVA’s lab has the support of information literacy specialists who help students develop a critical eye toward AI-generated outputs, questioning sources, identifying biases, and understanding model limitations. This is exactly the kind of competency the job market is starting to demand and that very few institutions are teaching in a structured way.
The four pilot projects already underway
The lab didn’t stay in the realm of ideas. There are already four pilot projects in progress, each approaching AI from a different angle and across distinct disciplines.
Economics and AI: code, ethics, and critical thinking combined
The first pilot, launched this spring, brings together an economics professor and three librarians in a course that combines hands-on programming with AI, critical thinking training, and discussions about ethics. The goal is to explore what it means to use AI tools responsibly and how these technologies could reshape employment, economic growth, and social inequality.
Writing and education: AI impacting high school classrooms
The second pilot places students from a first-year writing seminar in direct dialogue with students and teachers from a local high school. Together, they examine the impact of AI on teaching and learning. Working with an English professor and lab facilitators, the college students develop lesson plans that model a thoughtful, intentional integration of AI into high school classrooms.
Philosophy and critical evaluation of AI outputs
A third pilot, scheduled for the fall, will be led by a philosophy professor. Students will conduct projects exploring potential uses of AI across different areas of society, with a special focus on developing the skills needed to critically evaluate and validate the outputs produced by artificial intelligence systems.
Biochemistry with AI support
The fourth project, also scheduled for the fall, was developed by a professor of chemistry and molecular physiology and biophysics. The idea is to integrate AI-assisted learning directly into biochemistry courses, showing how the technology can function as a support tool for studying and research in the hard sciences.
Leo Lo emphasized that all of these pilots revolve around real problems. Faculty are asking how they can incorporate AI into teaching and learning. And students want to use AI to create something tangible — an artifact they can show future employers, demonstrating how they applied these tools responsibly and ethically.
Real competencies for a job market that never stops changing
When more than 10% of internship postings already ask for AI skills, we’re no longer talking about a competitive edge — we’re talking about a requirement that’s going to become table stakes in the coming years. The problem is there’s a massive difference between using an AI tool and understanding what it does, when it gets things wrong, what its limits are, and how to integrate it responsibly into a workflow.
That difference is exactly what separates someone who will stand out in the job market from someone who will depend on the tools without being able to assess the quality of what they deliver.
The AI Literacy and Action Lab bets on developing competencies that go beyond the technical. Critical thinking applied to technology, the ability to question a model’s results, understanding of AI ethics, and the skill to communicate to multidisciplinary teams what a tool can and cannot do are some of the competencies the program aims to cultivate.
This skill set is especially valuable because it doesn’t depreciate at the same speed as knowledge of a specific tool. When ChatGPT evolves into a more advanced version or when a new platform dominates the market, someone who learned to think critically about AI will adapt much more easily than someone who just learned to click the right buttons.
It’s also worth noting that the student profile that benefits most from this approach isn’t the computer science major, who already has other paths to learn about AI. The lab’s focus is specifically on broadening access to AI literacy for students across all fields, democratizing a conversation that’s still heavily concentrated among technical profiles. Companies in healthcare, law, communications, education, and countless other sectors need professionals who understand AI without necessarily being engineers or data scientists.
AI and the future of work: the question nobody can answer with certainty
One of the most interesting reflections emerging from UVA’s initiative has to do with the relationship between AI and employment. Christa Acampora was quite direct when addressing the topic: there’s a natural tendency in higher education to look at something new and say we need to study it in order to understand it. And there’s usually an assumption that more knowledge or more access will better prepare people for the job market.
But according to Acampora, the changes driven by AI may not follow the pattern of previous technological revolutions, where new jobs eventually offset the ones that were eliminated. That remains an open question.
That’s why the lab’s focus goes beyond technical training: teaching students to better understand their own human capabilities through the use of these tools. This has, according to Acampora, real pedagogical power — and it’s where attention should be focused.
Leo Lo complemented this view with a pragmatic perspective. He acknowledged that AI is far from perfect. The technology is constantly improving and changing, but it still makes plenty of mistakes. And even people who are critical of AI become stronger in their arguments when they truly understand the technology.
The idea, according to Lo, is for people to build this literacy so they can help shape the technology in the direction they want, rather than simply being shaped by it. Critical engagement, not blind adoption — that’s the core philosophy.
The social impact that extends beyond the classroom
When a university decides to structure AI education in a cross-disciplinary way, the social impact of that decision reaches far beyond the students who go through the program. Better-prepared professionals make better decisions, question the indiscriminate use of technology, and identify when an automated system might be perpetuating a bias or causing harm that isn’t immediately visible.
In sectors like public health, social services, K-12 education, and government policy, where AI tools are already being adopted at scale, this kind of critical training can literally change lives.
UVA’s initiative also shines a light on a debate that needs to happen far more often at educational institutions: what is the role of universities in the face of a technology that advances faster than any curriculum can keep up with?
The answer the AI Literacy and Action Lab proposes is that the role isn’t to teach specific tools — which will change — but to develop people capable of continuous learning, critical evaluation, and responsible action in the face of technologies we’re all still collectively learning to understand. That posture is, in itself, a form of hands-on learning applied to institutional reality.
The model also sparks a reflection about what other universities could do in this direction. The United States has a growing demand for professional qualification in technology-related fields, and initiatives that integrate AI into curricula in a critical and accessible way could generate significant social impact, especially in communities where access to specialized technical programs is still limited.
UVA’s experience isn’t a ready-made recipe, but it’s a clear signal that the path forward runs through integration, practice, and critical thinking — not by tacking on one more elective at the end of the semester. 🎯
