For university educators, the question is not whether students should learn about AI, but how we can ensure every graduate develops a meaningful level of AI literacy. AI education should be treated as a foundational requirement rather than a specialised topic. Building that capability across a curriculum requires more than introducing new tools and courses; it demands a coordinated effort that spans faculty development, course design and institutional leadership.
We began a university-wide effort to extend AI teachings across our curriculum in 2020. Here is some practical advice on how educators can embed AI literacy across disciplines and prepare graduates to join an AI-enabled workforce.
AI literacy is everyone’s responsibility
It’s a common debate in higher education at the moment: who should take responsibility for instilling AI literacy? The answer: everyone.
University leadership, faculty and administrators all play a role in ensuring that students are equipped with the skills they need. Ignoring AI readiness is a disservice for students.
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At my university, this shared responsibility has shaped an approach that focuses on three key components of AI education: fundamentals, ethics and application within a discipline. This framework for teaching students how AI systems work and how to use them responsibly ensures that ethical considerations are not treated as an afterthought. Instead, ethical awareness becomes inseparable from technical understanding and practical application.
Embedding AI across the curriculum
For many institutions, integrating AI learning beyond subjects like computer science or engineering is a challenge. The AI² Center oversees the AI Across the Curriculum initiative, which aims to bring AI learning into disciplines across the university. One of the first steps was ensuring that faculty were prepared to teach with and about AI.
With faculty training being the starting point for AI implementation, we also hired 100+ new faculty members with AI expertise across our 16 colleges, as opposed to concentrating them solely in STEM fields. These faculty act as knowledge hubs within their disciplines, helping colleagues integrate AI concepts into teaching and research.
To support course development, we introduced a system of AI course designations. Through the AI Designation for Courses, our AI Education Committee reviews undergraduate and graduate courses to ensure course labels reflect their level of AI content. Some courses incorporate AI tools into assignments, while others focus more on teaching how to analyse, or even build AI systems.
These course designations also allow for the tracking of how many of our students are exposed to AI learning and assess whether they are achieving desired outcomes.
Defining meaningful AI literacy
Determining what level of AI competency students should reach is not straightforward, particularly due to technology evolving so rapidly.
Rather than focusing on mastering specific tools, instructors should understand the importance of teaching durable foundations that students can build throughout their careers.
These foundations include understanding how AI models work, recognising their limitations and biases, and learning how to apply them appropriately within a professional context. Specific platforms or models may change from month to month, but the underlying principles remain crucial.
We reinforce this approach through programmes such as the AI Fundamentals and Applications certificate, which is an official university credential and available to undergraduate students of any major. Ultimately, the strongest sign for institutional success will be when AI learning becomes embedded in required courses, rather than remaining elective.
Hesitations and misconceptions
While students are often eager to experiment with and learn from new technologies, faculty can be slower to adapt. Their concerns tend to stem from unfamiliarity with AI tools or uncertainty on how to ethically apply them to coursework.
Exposure and first-hand practical demonstrations can help. Even briefly demonstrating how AI can assist with routine tasks can shift perceptions.
However, this does not mean that adoption of AI should be forced. Instead, make space for academics to form their own opinions on AI, based on experience and in-depth understanding.
This approach also extends to academic integrity concerns. Rather than framing AI primarily as a tool that can be used for cheating, educators should clearly define assessment methods and communicate when and how AI can be used in coursework.
Choosing tools without losing focus
Another challenge universities face is deciding which institution-wide AI platforms to adopt. With new tools constantly emerging, there can be pressure to select the “best” technology. But do not let this decision overpower educational strategies. Tools are secondary to educational goals.
Security and data privacy should still guide institutional decisions. For example, at my institution, certain AI tools cannot be used with university data unless they operate within secure environments designed to protect sensitive information.
But providing access to tools alone is not enough to establish AI literacy. Simply purchasing a university-wide licence for a popular platform does little if students and faculty are not taught how to use it thoughtfully.
Collaboration over competition
As universities explore AI initiatives, we all benefit most when institutions share knowledge rather than compete for it.
We regularly host delegations from other institutions interested in learning about its AI initiatives. The AI² Center also hosts an annual AI² Summit, where other universities can seek guidance on implementing similar programmes and learn to adapt aspects of the AI Across the Curriculum model to suit their own needs. We also run the AI Visit programme that brings groups of up to 30 participants from other universities to the Gainesville campus for guided sessions on AI-enabled teaching methods, discussions with faculty leaders and a behind-the-scenes tour of HiPerGator.
We believe AI education should be collaborative, and these exchanges reflect this.
For higher education institutions navigating rapid technological change, that collaborative mindset may prove just as important as any individual tool. By sharing experiences and approaches, universities can collectively move closer to a future where AI literacy is a core element of every student’s education.
Hans van Oostrom is director of the AI² Center at the University of Florida.
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