Picture the AI in education conversation as a Venn diagram. One circle represents how students use AI: what they are doing with it, how much they are using it, and for what purpose. The second is our response as educators and institutions: policies, assessment redesign, academic integrity, etc. Most of the debate lives inside those two circles, and they are all important. Yet, there is an intersection where they overlap, and it holds a question we rarely stop to ask, one that belongs to both circles at once. Which students get to become good at this, and what is our responsibility to the ones left to navigate it alone?

That intersection is where equity sits, and it is the part of the AI literacy conversation that most often goes missing.

AI literacy is fast becoming an invisible curriculum. It shapes academic outcomes, yet it is largely transmitted informally, through friends, family, prior schooling, and the confidence that comes from having the time and the networks to experiment. Students who arrive already fluent pull ahead; students who do not are left to close the gap without scaffolding. When a skill this consequential is acquired socially rather than taught, it stops being a neutral technology story and becomes an equity one. As we mention in the toolkit, this is a pedagogical gap, not a technology one.

As part of addressing this problem I co-facilitated (as a member of the RSB Bioscience Awarding Gap Network) a workshop at the Minoritised Life Scientists Future Forum (MLSFF26) in 2026 in Edinburgh. The session (The AI Literacy Equity Challenge – How can we ensure that all learners benefit from AI tools? ) and the toolkit that grew from it start from a simple premise. If AI literacy influences academic outcomes, it should be taught explicitly and designed equitably, not left to chance.

I do not want to downplay how difficult the conversation is. There are at least three real tensions pulling against one another. Students need the intellectual agency to evaluate and challenge what AI produces, not simply to generate with it. There is a legitimate worry about AI use, as it rewards production over understanding; students can produce without learning. There is also the tension that nobody names often enough: educators’ time. The technology moves in weeks; our curricula, committees, and workload cycles move in terms and years. Waiting for the perfect institutional response is entirely understandable. However, the cost of waiting is that AI literacy continues to be acquired informally, by the students who were already best placed to acquire it.

The toolkit is built with that reality in mind. It does not ask you to become an AI enthusiast, nor does it assume that more AI in the classroom is better. It does not ask you to redesign your programme. It offers a set of starting points — a scaffolding framework, an integration design canvas, an equity rubric, ready-to-use activities with built-in scaffolding, sample module statements, and an equity checklist — that you can pick up and adapt within the time you already have.

If you’re unsure where to start with AI in your teaching practice, I recommend this short exercise. Find a half-hour slot in your calendar, and think about one assessment in your module and how a student may use an AI tool. Then, consider what clear instruction about your expectations of their use of AI in this assessment would look like. You do not need to change anything yet. Just notice what you find.

This is the same work many of us have always done with our teaching. Making the implicit explicit, with clear instructions, so that a student’s background does not quietly determine the outcome. AI is new. That task is not.

A fuller description of the workshop and the evidence supporting this conversation is published in The Biologist, the Royal Society of Biology magazine, under the title ‘Ensuring AI literacy for all‘

The AI Equity Toolkit Resources

If you would like to run this in your own context – with your programme team, at an away day, or as a staff development session – the full workshop, including the slide deck and toolkit, is yours to adapt and make your own.

Please do share what you try and, importantly, what your students say.

A note on how this resource was made: The activities in the AI toolkit and workshop are original, built on published AI literacy frameworks. We have used GenAI tools as a drafting and editing aid in writing them up, as I do across this site. The resource is shared under a Creative Commons licence, so you’re free to adapt it. A fuller account of how I work with GenAI tools can be found here.