A programme-wide approach to AI literacy

Academics feel very differently about the role of generative artificial intelligence in teaching and learning. Although some are reluctant to use the technology, others develop their knowledge and use it to enhance their practice. Students notice these distinctions.

For some, they can cause a confusing and inconsistent experience: one module might encourage experimentation with AI, while another might discourage or even prohibit its use. Rather than leave students to navigate these contradictions on their own, staff must ask themselves whether they are being clear about how, when and why to use AI. 

A programme-wide approach can help. Rather than treating AI as an isolated topic, educators can develop students’ AI literacy through six connected capabilities.

1. Can students use AI ethically?

The first step is to help students understand that AI use is not simply a technical activity. They need to consider privacy, bias, intellectual property, confidentiality, academic integrity and the potential consequences of their decisions.

For example, a business student might use AI to analyse customer data or develop a marketing campaign. But can they identify what information they should not give an AI tool? Can they recognise bias in the output? Can they explain why a particular use of AI is ethical?

Ethical AI should become part of ordinary disciplinary learning.

2. Can students question AI?

An AI-generated answer can seem convincing yet be incomplete, biased or simply wrong. Students therefore need to develop critical AI literacy.

This means asking students to verify claims, examine assumptions, identify missing perspectives and compare AI-generated information with credible evidence. In a business strategy class, for instance, students could ask an AI tool to analyse a market before independently checking the output’s assumptions, evidence and conclusions.

Rather than produce a better AI response, the aim is to develop the ability to judge the credibility of the response.

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3. Can students recognise where humans matter?

AI can generate ideas, summarise information and identify patterns, but professional practice still requires judgement, contextual understanding and accountability.

Students need opportunities to explore situations where an AI recommendation might be technically plausible but professionally inappropriate. A management student might use AI to generate strategic options but must then decide on the appropriate option for a particular organisation, workforce or market.

This shifts the focus from “What can AI do?” to “What should a professional do with AI output?”

4. Can students apply AI to real problems?

AI literacy becomes meaningful when students use it to address authentic problems rather than simply complete artificial exercises.

Students might have to use AI to explore a business problem, develop possible solutions and then document their decision-making. They’d have to explain what tool they used and why, what it did well, where it failed and what they changed.

This approach turns students into active participants rather than passive consumers of AI-generated content.

5. Can students reflect on their AI use?

Students also need to understand how AI is changing their thinking.

Did it help them understand the problem, or simply complete the task faster? Did they challenge the output? What did they contribute that the technology could not? What would they do differently next time?

Reflection makes AI use visible. It also helps students recognise the difference between using AI to support and bypass learning.

6. Can assessment still test ability?

By banning AI, we risk underpreparing students for workplaces in which AI will be increasingly present. At the same time, allowing unrestricted AI use can make it difficult to measure learning. A solution is to design assessment around judgement, application, critique, decision-making and reflection.

For example, rather than asking a business student to submit a conventional strategy report, an assessment could require them to use AI to generate initial ideas, evaluate those ideas against credible evidence, identify weaknesses, develop their own recommendations and explain the decisions they made. The assessment would evaluate not just the final product but students’ thinking.

AI tools will continue to change, and today's technical knowledge can quickly become outdated. What will remain valuable is the ability to use AI ethically, question its outputs, exercise human judgement, apply it to meaningful problems and reflect on its influence on our thinking.

That is why AI literacy should not sit at the margins of the curriculum or depend on individual academics but should follow a programme-wide approach that maps AI capabilities across the student journey. Each stage of the programme can introduce and develop different aspects of AI capability, building on what students have already learned.

The goal is to develop graduates who can think critically and act responsibly in an AI-enabled world.  To achieve this, we must prevent them from using the machines to think for them, and instead show them how to use the machines to enhance their thinking.

Patrice Seuwou is associate professor of learning and teaching and director of the Centre for the Advancement of Racial Equality at the University of Northampton.

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