In 2025, Jisc launched the AI in Marking and Feedback Pilot, a year-long initiative bringing together colleges and universities to explore whether AI can meaningfully reduce marking and feedback workload in a way that is acceptable to key stakeholders.
The project spans two strands — tools designed specifically for educational purposes (these were Graide, Keath and TeacherMatic), and general-purpose AI tools (such as ChatGPT, Gemini, and Copilot), where the project focuses on custom assistants.
Across the initial months of the pilot, insights have been collected via regular community sessions, feedback forms and one-to-one interactions. As common themes appear, we’d like to share insights with the wider Jisc membership, allowing learnings to translate to direct value for the sector.
As such, we’re publishing this series of blogs, which we hope will give you a useful window into what the pilot has revealed about the role of AI within marking and feedback. You can read all the blogs in this series by following the links on this parent page.
The Value of Student Consent
Consent has been one of the most widely discussed issues in the AI in Marking and Feedback Pilot. In what circumstances should students be able to choose whether their work is processed using artificial intelligence?
As conversations progressed, two key factors became apparent. First, there is the legal question of whether processing student work with AI is compliant with UK GDPR. Second, there is the question of whether students and staff feel comfortable, informed and respected.
From a data processing perspective, participants reached different conclusions on the validity of consent as a lawful basis for processing data in this instance.
Alongside these legal considerations, however, participants’ decisions were also shaped by the “hearts and minds” side of consent. Institutions found that students often worried not just about data processing, but also about fairness, trust, and the fear that AI might replace educators or shrink the sectors of the labour market in which they hope to build their careers.
Accordingly, some institutions judged that requiring consent was appropriate from a student engagement and relationships perspective. In practice, this meant adopting either an opt-in or an opt-out approach. While this did require additional logistical effort, with sufficient foreplanning these processes were generally manageable.
Where institutions decided to seek student consent, the next step was to consider how the pilot should be communicated so that students could make an informed decision about whether to participate.
Our experiences from the pilot suggest that hearing about the initiative directly from the relevant educators tends to be the most effective approach. These are the people best placed to explain the intended benefits, the procedures involved and any risk-management measures in place in a clear and practical way.
Wider engagement activities have also proven helpful. Several participating institutions convened student advisory panels, town-hall style sessions, or meetings that included student unions. These conversations provided a space for questions and concerns to be raised early, and in many cases helped institutions refine their approach to communication, implementation and governance.
Questions remain as to how student consent-driven models of AI implementation will scale in cases where AI use becomes standard practice, rather than part of time and scope limited pilots. That said, I think there are two widely applicable takeaways to be drawn.
Firstly, where colleges and universities are conducting their own pilots of AI (particularly where the use of AI could be considered sensitive or controversial), consider that an opt-in or opt-out approach could help to engender trust, and establish an ethos of the pilot being conducted with students, rather than upon them.
Secondly, regardless of whether opt-in or opt-out approaches are decided upon, clear dialogue with students – and also staff – is invaluable in developing trust. Reflect on and communicate how each stakeholder group will benefit from the initiative, what risks they may experience, and how these risks will be mitigated. Listen to and take on board people’s feedback. And be creative and diligent in how different stakeholder groups are reached.
Find out more by visiting our Artificial Intelligence page to explore publications and resources, learn more about our communities and sign up for our AI Literacy training.
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Get in touch with the team directly at AI@jisc.ac.uk
