Gary C Wood

Last week, I attended a symposium on AI-assisted feedback at Imperial College London. I was particularly inspired by an opening panel presentation by Professor Naomi Winstone on the care-full craft of feedback in the age of generative AI. One question in particular stayed with me: what if the problem isn’t that we’re doing too little feedback, but that we’re doing too much of the wrong thing?

Reflecting on that question since, I realise that in UK higher education, we have become somewhat obsessed with feedback volume. More comments, we assume, signals more care. Yet the National Student Survey continues to tell a familiar story: feedback remains one of the lowest rated aspects of the student experience. If our carefully crafted comments aren’t landing – if students aren’t using them to change what they do – then we haven’t really provided feedback at all. We’ve provided inputs.

Much of the current focus on generative AI in feedback is driven by staff workload. AI promises quicker turnaround, greater consistency, and more manageable marking. Timeliness does matter – but efficiency alone is a staff facing driver.

As the Manifesto for Feedback in the Age of Generative Artificial Intelligence argues, feedback is not a product but a relational, developmental process; comments only become feedback when students engage with them and something meaningful changes as a result (Winstone et al, 2025). That distinction matters now because generative AI makes it trivially easy to scale feedback production. If we automate poorly designed feedback processes, we risk amplifying – at scale – the very practices students already find least useful.

If we simply use AI to produce the same kinds of comments more quickly, we are doing the same things differently, but not doing different things.

From fixing the work to developing the learner

In my work with Beverley Gibbs on Students as Partners, we found that students conceptualise feedback in a fundamentally different way from academics. They expect feedback to help them adjust course while they are in flight, not simply to explain why they missed the destination once they’ve landed (Wood & Gibbs, 2019).

To make sense of this difference (and other gaps between staff and student conceptualisations of the learning experience), we drew on the distinction between single loop and double loop learning, originally articulated by Argyris & Schön (1978), and later applied to learning and feedback in higher education contexts (Gibbs & Wood, 2021).

Most feedback students receive operates in what Argyris & Schön describe as single loop learning. It is a tactical correction designed to help a student fix a specific error in a specific task: improve the structure, strengthen the argument, engage more critically with the literature, etc. Such feedback can improve this piece of work, but it rarely changes how the student approaches learning more fundamentally.

Double loop learning works at a different level. Rather than simply asking how do I fix this?, it prompts the learner to reflect on why this kind of issue is occurring. In feedback terms, this might involve helping a student recognise that difficulties with critical analysis, academic voice, or synthesis appear across multiple modules – not as isolated mistakes, but as patterns in how they approach their learning.

The challenge is not that this kind of reflection lacks value. It’s that our feedback systems are rarely designed to support it. Feedback is fragmented across modules, markers, and assessment points. Individual tutors are constrained by what they can see, and students are left to do the hard work of synthesis themselves – often without the support, confidence, or feedback literacy to do so effectively.

The “all knowing” digital tutor

This is where generative AI opens up a genuinely new possibility.

Imagine a student owned feedback repository that captures feedback across modules and across time. A generative AI system could help students identify recurring strengths, persistent challenges, and priorities for development – connections that human tutors, constrained by module and programme boundaries, simply cannot see.

Now imagine that same system sitting alongside a new assessment brief. It could prompt students to ask: What have I previously been told to do less of? Where does this task give me an opportunity to practise something I struggled with before? What examples of good practice from earlier work should I carry forward?

Here, AI is not generating more feedback. It is helping feedback do more work. It enables feedback to function in exactly the way many students already expect it to: as guidance for future action, not post hoc justification.

A provocation for the sector

The COVID 19 pandemic forced us to do things differently, and in doing so we discovered that some forms of disruption led to better practice. AI is a similar inflection point. The risk now is that we use it merely to formalise existing institutional desire lines – automating and entrenching feedback processes that already fail to meet students’ needs – rather than redesigning the landscape around how students actually want to use feedback.

If AI becomes little more than a faster way of producing comments, we will have missed a significant opportunity. The real challenge for the sector is to stop asking how AI can help us do the same things more efficiently, and start exploring how it allows us to design feedback differently – around use, patterns, and future learning.

AI is not just a faster pen. It’s an opportunity to reimagine what feedback could be. Are we brave enough to design for it?

References

Argyris, C. and Schön, D.A. (1978) Organizational learning: A theory of action perspective. Reading, MA: Addison Wesley.

Gibbs, B. and Wood, G.C. (2021) ‘How can student partnerships stimulate organisational learning in higher education institutions?’, Teaching in Higher Education. https://doi.org/10.1080/13562517.2021.1913722

Winstone, N., Gravett, K., Noble, C., Nicola Richmond, K., Bearman, M., Jensen, L.X., Jones, A., Corbin, T., de Kleijn, R., Gabelica, C., Kainth, R., Poobalan, A. and Reedy, G. (2025) Manifesto for feedback in the age of generative artificial intelligence. Figshare. https://doi.org/10.6084/m9.figshare.30195568

Wood, G.C. and Gibbs, B. (2019) ‘Students as partners in the design and practice of engineering education: Understanding and enabling development of intellectual abilities’, in Malik, M., Andrews, J., Clark, R. and Broadbent, R. (eds) Realising Ambitions: 6th Annual Symposium of the UK & Ireland Engineering Education Research Network. Portsmouth: University of Portsmouth.

Last week, I shared some guidance for students facing take-home or online open book exams, focused on how to revise and prepare effectively. In today’s post, I offer you some advice for the day of your assessment itself, with seven tips to help you do your best.

Your normal routine when you’re working at home might not be to eat breakfast, or to have meals at different times than you would during a day working on campus. But your exam is going to require you to think hard, and that uses more energy than many physical activities. Making sure you’ve had something nutritious to eat will help you maintain concentration without feeling tired as you’re working.

You won’t have time in the assessment to look everything up, and to learn about topics that you have not revised. That’s why I recommended making sure you have a good overview of the material, have thought about the connections within the material, and have prepared a prompt sheet with key equations and details.

In the exam itself, resist the temptation to start checking your notes as soon as you see the questions. Instead, trust what you’ve learnt, as if you were in an invigilated exam and didn’t have your notes, and map out your answer using that knowledge. Use your notes only to check facts or details that you really can’t remember. If you do need to consult your notes, use the indexing you’ve created (see last week’s post), and pull out what you need, then focus on writing your answer. Don’t keep going back to your notes to look things up – it’ll interrupt the flow of you writing a well-structured answer.

If you find that there’s a question that you really don’t know how to answer, leave it to the end, and come back to it. Do all of the other questions, and then you can use any remaining time to revisit the question you couldn’t answer, and consult your notes and books.

If you’re sitting a take-home paper with a submission window of, say, 24 hours, you are not expected to spend all of the time working on your answers. Your tutor may indicate how long they expect the paper to take you, but as a guide, it’s unlikely to be much longer than your originally scheduled invigilated exam. So, don’t spend a lot longer and end up turning your exam into coursework. Bear two things in mind to help with this.

First, your tutors are expecting you to submit exam answers, not polished pieces of coursework. You need to present your ideas clearly, using the right content and with logical structure. But spending hours redrafting the text so it says the same things more eloquently, or finessing presentation, will not lead to higher marks.

Second, you are not expected to undertake literature research in the exam. You are being tested on what you’ve learnt during the course, not on what additional learning you can cram into the assessment period. Doing loads of additional reading may decrease the clarity of your answer, as you squeeze more in, and bury the key points from the core learning material that your tutor is looking for. Stick with what you know, and use that competently to demonstrate your learning. Good marks will follow this, not masses of extra detail.

You’ve probably had the seriousness of plagiarism drilled into you, and know how to avoid it in coursework. But under the pressure of a timed exam where you have your notes, books and even the Internet as reference material, it can be easy to commit accidental plagiarism. Take special care of copying and pasting material, or reading and then using material in your own work. If you copy and paste or paraphrase text, use a different colour type, so that you remember to reference it properly before you submit.

One of the benefits of doing your assessments in a traditional exam hall is that distractions are minimised. Give yourself the best chance in the assessments you are completing at home by establishing conditions that will allow you to be highly focused.

Turn off your phone, and close any social media applications and your email on your computer. If you think you’ll struggle to avoid accessing these whilst you are doing the exam, you might benefit from downloading an app that will block them – see this website for some suggestions and reviews.

Also think about distractions at home. Tell other people in your household that you are doing an assessment, let them know when, and ask them not to disturb you. Consider making a polite notice for your door to remind your family or housemates – putting up the notice can also be a good cue to yourself that you are entering exam mode, and need to focus.

It probably sounds obvious, but it’s important to make sure you submit your work on time. So, don’t leave it too late in your allotted time to start scanning or capturing your work, saving your file, and uploading it, to ensure that you don’t go over time.

Exams under any circumstances can be stressful. On campus, you’d probably manage this by meeting up with friends afterwards, so that you can reassure each other by discovering that you all found particular questions difficult, and by comparing what you wrote for different answers. Don’t underestimate the importance of doing this, and of maintaining a social connection, even though you’ll be isolated from your friends as you sit the exams.

Plan ahead, and think about when you might check in with each other afterwards. Schedule this in advance, so that you know you won’t be disrupting each other trying to work, and then meet online to debrief and decompress as you usually would face to face.

Although the style of assessment you are facing during the COVID-19 pandemic is probably different from usual, remember that it remains simply an opportunity for you to demonstrate your learning. Keep calm, and read the questions carefully. Once you think you understand what to do, read the question again to check that’s what it’s asking for. And then do your best – that’s all you can ask of yourself. You can do this, and by the time you come out the other side, you’ll have a great example of your resilience and ability to cope under pressure with short deadlines for your next interview! Good luck!

COVID-19 is having a major impact on higher education. After an initial response focused on supporting students’ wellbeing and shifting teaching online, many educators are now turning their attention to assessment. Perhaps the most challenging assessment to transition into a digital format is the traditional, invigilated exam. 

One solution is to substitute the traditional exam with a take-home exam paper. Take-home exams are assessments closely related to traditional exams, but with an important difference in that the question paper is released to students at a scheduled time, and students choose when to work on it before they submit their answers after a fixed period of time – often 24 hours. 

Working without supervision or invigilation means that students can consult course materials, textbooks, their notes, and the Internet as they answer. Maintaining rigour, reliability and validity therefore means that we cannot simply take our prepared exam papers, designed to be sat under invigilated exam conditions, and release them online. We need to rethink the type of questions we ask, and the way we present them to students.

At the University of Sheffield, Dr Beverley Gibbs and I have prepared a short paper with our Faculty of Engineering colleagues, in which we have shared some considerations and suggestions for colleagues on how to adapt a planned formal exam into a take-home paper. We share below a version of this work, for the benefit of the wider community. Although our context is engineering, this guidance has wide applicability, and we hope it will be of use to educators across all disciplines where traditional invigilated exams are used.

Please feel free to download, adapt and use this paper in your own contexts. In return, please tell us in the comments below if you find this useful or have questions, and do share your own ideas and suggestions with us.

Wood, G.C. & Gibbs, B. (2020). COVID-19 Crisis: Transitioning from traditional invigilated exams to ‘take-home’ exam papers. Sheffield: University of Sheffield.