Move beyond technical skills and embed AI literacy that teaches students to question

Universities have made significant progress on AI literacy. They have developed guidance, redesigned assessments and invested in staff and student development.

But much of this work still treats AI literacy as a set of technical competencies: writing effective prompts, identifying hallucinations, recognising bias and verifying AI-generated content.

These are essential skills, but they are no longer enough. If higher education is to prepare graduates for AI-enabled societies, AI literacy must move beyond using AI responsibly to understanding how AI is shaped by social, political and institutional choices – and how those choices can themselves be challenged.

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Drawing on ideas from responsible innovation, we argue that AI literacy should equip students not only to use AI, but also to participate in the collective stewardship of AI futures.

At our university, we have been exploring what this broader approach to AI literacy looks like in practice. Rather than treating AI as a neutral technology, we encourage students to examine the assumptions, values and trade-offs embedded within AI systems, recognise where technical questions become political ones, and develop the confidence to imagine and create alternative futures. Here are four ways universities can begin to embed this approach.

1. Teach AI as a socio-technical system

Students need to understand that AI is more than a technology. It is a socio-technical system, shaped by social, political, organisational and material factors, while also influencing how societies make decisions and allocate resources. AI depends on data centres, global supply chains, environmental resources and often invisible human labour.

Data offers a useful entry point. It does not simply represent reality but reflects decisions about what is measured, how categories are defined and whose knowledge counts. These choices shape the datasets used to train AI long before a model is built.

An AI system designed to support sustainability, for example, embodies assumptions about what sustainability means and how it should be achieved. Different assumptions lead to different datasets, different systems and, ultimately, different futures.

Teaching AI as a socio-technical system means asking not only how AI works, but how it came to take this form, whose interests it serves and what futures it makes possible.

Try this: Ask students to analyse an AI application in their discipline by asking: What assumptions underpin this system? Whose knowledge does it privilege? Who is excluded? What futures does it make more likely?

2. Move beyond technical fixes

Many AI literacy initiatives focus on better prompting, reducing bias, improving explainability and keeping humans “in the loop”. These are valuable skills, but they can imply that AI’s challenges are primarily technical.

Many decisions about AI are not technical at all. Better data or more accurate models cannot determine whether an AI system should be used, what purposes it should serve or who should make those decisions. Even if a facial recognition system were to perform equally across demographic groups, questions about whether it belongs in schools or public spaces remain ethical and political.

Universities should help students recognise when political choices have been framed as technical problems and encourage them to ask whose interests, values and perspectives are being prioritised.

Try this: Ask students to identify one important question about an AI system that cannot be answered with better data, a better model or greater human oversight.

3. Design learning that cultivates agency

Much AI literacy prepares students to adapt to AI. Universities should also help them see that AI systems can be questioned, redesigned or rejected. AI is continually shaped through organisational decisions, professional practice, regulation and public participation.

Learning should therefore focus on where change happens. Students might critique institutional AI policies, redesign AI-supported services or work with external partners to explore alternative approaches. Case studies of organisations or communities that have successfully challenged or reshaped AI systems can reinforce that technological trajectories are negotiated rather than inevitable.

Try this: Ask students to map everyone who can influence an AI system in their discipline. Where are the opportunities to challenge, redesign or redirect how it is developed or used?

4. Make futures thinking part of AI literacy

Universities can make a distinctive contribution by helping students recognise that AI futures are not predetermined. Rather than assuming society must adapt to emerging technologies, students should be encouraged to ask whether systems could be designed differently, whether AI should be used at all and whose voices should shape those decisions.

This shifts AI literacy from predicting technological change to imagining alternative futures grounded in values such as care, sustainability or equity.

Try this: Ask students to imagine futures that are not constrained by today’s technologies or institutions. What becomes possible when we stop treating current AI trajectories as inevitable?

These approaches move students from competent users of AI to active participants in shaping its future. Universities have always done more than prepare graduates for employment. They also equip them to question assumptions, navigate complexity and imagine better futures. AI literacy should reflect that wider mission.

Katie Ledingham is senior lecturer in responsible and transformative innovation, and Sarah Hartley is professor of technology governance, both at the University of Exeter Business School.

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