Last year, Jisc launched a pilot to explore how AI could help reduce workload around marking and feedback. That work continues this year, with KEATH, Graide, and TeacherMatic being piloted across more than 30 participating colleges and universities.
We’re now launching a new strand of the pilot — one that will see participants using general-purpose AI tools such as ChatGPT, Claude, Gemini, and Copilot to support marking and feedback. You can learn more about this pilot in our announcement blog.
Central to this new strand will be the good practice toolkit, which will include guidance on how to create Custom GPTs and make them secure. In parallel, we are also publishing good practice case studies from colleges and universities whose innovative approaches have inspired and shaped this pilot.
In this post, you’ll learn about the pioneering work that Kirklees College has been doing around AI in marking and feedback. You may also be interested in a parallel post, exploring London South Bank University’s work in this area.
Kirklees College – a scalable approach to creating Custom GPTs
At a Glance
- Over 130 bespoke Custom GPTs were created, each aligned to specific units, assessment criteria, and awarding body standards
- An estimated reduction of 10 – 15 hours per week in marking, moderation and admin tasks, per member of staff (based on initial reviews before full roll out)
Overview
Kirklees College has developed a comprehensive model for integrating generative AI into vocational assessment. The initiative, which is in the process of being rolled out across the college, will see widespread deployment of Custom GPTs for feedback generation, criteria mapping, and moderation support, alongside Microsoft Co-Pilot for streamlining documentation and reporting.
Key to their approach is the bespoke nature of each custom tool. All founded upon a centralised template, over 130 bespoke Custom GPTs have been created so far, each aligned to specific units, assessment criteria, and awarding body standards. The project’s lead, Paul Scott, has led the development and testing of this suite of tools. And, having conducted initial reviews with colleagues, is overseeing the scaling of the initiative across the college.
The primary aim is to reduce staff workload while improving the quality, consistency, and turnaround time of feedback. The initiative also seeks to enhance learner understanding, support self-reflection, and strengthen academic integrity through transparent, standardised uses of AI.
Impact
Following initial reviews, educators reported substantial reductions in administrative effort and marking time, while learners benefited from faster, clearer feedback. The use of GPT templates streamlined setup for new units, allowing scalability across disciplines.
Impact was observed through direct workload analysis and qualitative feedback from staff and students. The evidence pointed to stronger engagement with assessment expectations and demonstrable gains in learner outcomes.
Rollout and Engagement
Implementation is following a phased approach. GPTs were first designed, tested, and refined during the initial development phase. A pilot was conducted with four staff across two departments, which will be followed by a full departmental rollout. Faculty-wide deployment and awarding body engagement are planned for late 2025, with institution-wide implementation continuing through 2025–26.
All staff using GPTs are required to attend a standardisation meeting to ensure consistent practice. These sessions are now linked to formal awarding body standardisation and moderation training, embedding AI practice within existing quality assurance frameworks.
Challenges and Resolutions
Data confidentiality is being addressed through anonymised workflows and robust data governance protocols.
To build staff confidence, targeted workshops and pilot training were delivered.
Scalability was achieved by developing flexible GPT templates that could be cloned and adapted for new units.
Learner access was carefully managed, with time-limited GPT use ensuring both compliance and autonomy.
Next Stages
Future plans include continued updating of GPTs in line with awarding body guidance, faculty-wide launch in late 2025, and full institutional rollout across the 2025–26 academic year. The college will maintain its focus on moderation alignment, staff training, and scalability through the ongoing refinement of templates and standardisation processes.
In Action
In practice, the AI marking workflow begins with a tutor reviewing the assignment brief using a GPT to confirm alignment with learning outcomes and assessment criteria. When a learner submits work, the GPT assesses the submission against designated criteria and generates structured comments. The assessor validates this feedback to ensure quality and consistency.
During moderation, a separate GPT supports verification of marking decisions and standardisation across assessors. Learners are then given up to 48 hours of access to a self-assessment GPT, allowing them to review feedback, reflect, and refine their work before final submission. This structured process promotes fairness, transparency, and improved learning outcomes.
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
