Are we preparing students for the world they are actually studying in?
There was a time when “study skills” meant a fairly familiar set of things such as note-taking, revision techniques, essay writing, referencing, organisation and time management. Those things still matter. They still underpin successful learning. But the context in which students are studying has changed dramatically, and our idea of study skills needs to change with it.
AI is no longer a future issue. It is already part of our students’ everyday academic lives. HEPI’s latest Student Generative AI Survey found that 95% of students now use AI in at least one way, and 94% say they use generative AI to help with assessed work. At the same time, 68% believe understanding and using AI effectively is essential to thrive, yet only 48% feel staff are helping them develop those skills for the future. That gap is striking. It suggests that students are already navigating an AI-shaped learning environment, while many of our support models are still built for a pre-AI world.
That is why I think we need a new study skills agenda: AI literacy.
Not AI literacy as a one-off lunch or twilight session. Not AI literacy as a warning slide on plagiarism. And not AI literacy reduced to “how to write a better prompt.” What we need is something much broader than that: a more thoughtful, more critical and more educational response to the reality students are now working in.
The wider literature is increasingly pointing in this direction. UNESCO’s guidance on generative AI in education argues for a human-centred approach that develops human capacity, judgement and agency rather than simply reacting to the arrival of new tools. Jisc is making a similar case in the UK context, recommending that learners are supported to build AI awareness and literacy alongside academic integrity and wider study skills. This matters because it reframes AI literacy not as an optional extra, but as part of what it now means to learn well.
Crucially, AI literacy is not just technical knowledge. It is not enough for students to know that a tool exists, or even how to use it. They need to understand what it is doing, where its limitations lie, how reliable its outputs are, when its use is appropriate and when it crosses ethical or academic boundaries. A recent systematic review of AI literacy literature identified recurring themes including recognising AI, understanding it, applying it, evaluating it, creating with it and navigating it ethically. That is a much richer conception than simple tool use, and it feels much closer to the kinds of critical capabilities education should be developing anyway.
This is where the conversation becomes especially relevant for educators. Students are not only asking, “Can AI help me do this?” They are also asking, “Am I allowed to use it here?”, “Can I trust the output?”, “What counts as my own work?” and “Could I be accused of cheating even if I was trying to use it responsibly?” Those are no longer niche questions. They are study skills questions.
You might have recently been asked one of these questions yourself. Consider how confident would you be in answering these?
Jisc’s 2025 report on student perceptions of AI highlights concerns around misinformation, deepfakes, privacy and future employability. Students want support and they also want honesty. They know AI can save time and support learning, but they are also aware that it can mislead, oversimplify, and sometimes weaken independent thinking if used uncritically.
That is why “just let them figure it out” is not the mindset to have. We do not expect students to absorb referencing conventions by osmosis! We do not assume they will naturally become strong critical readers without modelling and practice. We teach, scaffold and revisit. AI literacy deserves the same treatment.
There is also a strong case for treating this as an equity issue. When institutions leave AI development to chance, the students who already have confidence, access and informal support move ahead faster, while others are left uncertain about what is allowed, how to use tools critically, or whether using them at all is risky. HEPI’s latest findings suggest students want more support in this space, not less. If AI literacy becomes essential but unevenly taught, then existing inequalities may simply be reproduced in a new form.
So what might a new study skills agenda actually include
For me, it means helping students learn how to question AI outputs rather than accept them too quickly. It means showing them how to verify claims, spot hallucinated references, compare outputs with trusted sources and make informed decisions about when AI is useful and when it is not. It also means being far clearer about the boundaries of acceptable use. Ofqual’s recent guidance is particularly helpful here. It emphasises the importance of actually talking to students about AI in coursework, rather than assuming they understand the rules. That is a simple point, but an important one: ambiguity creates anxiety, and anxiety often leads to poor decisions.
It also means teaching AI literacy as something more than compliance. Yes, students need to know what not to do. But they also need positive models of what good use looks like. They need examples of how AI can support brainstorming, planning, questioning, summarising, checking understanding or comparing perspectives without replacing the thinking they are there to develop.
Before using AI, I would encourage both students and staff to ask:
What is the purpose of using it here?
Is this use allowed for this task?
Am I sharing any sensitive or personal data?
How will I verify the output?
Can I explain what I kept, changed or rejected?
Is this helping me learn, or only helping me finish?
In practical terms, there are some straightforward starting points. We can build AI guidance into existing study skills sessions rather than waiting for a separate course. We can normalise verification as a routine part of using AI outputs. We can make acceptable use more assessment-specific and less vague. We can talk explicitly about privacy, especially where personal, employer or placement-related data is involved. And we can keep returning to the same core question: is this helping the student learn, or just helping them finish?
That question sits at the heart of this issue for me. The goal of AI literacy should not be to make students more dependent on AI. It should be to help them become more discerning, more ethical and more capable in a world where AI is already present. If study skills are meant to prepare learners for the realities of contemporary education and work, then our study skills agenda needs to catch up with reality.
Reference List
Stephenson, R. and Armstrong, C. (2026) Student Generative AI Survey 2026. Higher Education Policy Institute.
Jisc (2025) Student perceptions of AI 2025.
Ofqual (2026) Talking to students about AI in coursework: why the conversation matters.
UNESCO (2023) Guidance for generative AI in education and research.
Almatrafi, O., Johri, A. and Lee, H. (2024) A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023).
