Gloria Mendoza / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/
ollowing on from my last post around co-design, there seems to be an increasing number of experiments with educational institutions developing their own applications, rather than being totally dependent on commercial ed tech providers.
One area attracting attention is assessment where teachers and trainers in vocational education are used to working with colleagues, especially from examining bodies, in designing assessment rubrics and processes.
Last year, the UK Jisc organisation launched a pilot to explore how AI could help reduce workload around marking and feedback. That work continues this year, they say, with the KEATH, Graide, and TeacherMatic applications being piloted across more than 30 participating colleges and universities.
But they are now launching a new strand of the pilot using general-purpose AI tools such as ChatGPT, Claude, Gemini, and Copilot to support marking and feedback. Tom Moule from Jisc reports that Kirklees College has developed a comprehensive model for integrating generative AI into vocational assessment. Jisc explain that "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."
Tom Moule explains the process in developing the tools:
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.
With the growing release of open source Generative AI models and especially of Small Language Models we can expect to see more such experiments in co-developing AI apps for vocational education and training in the near future.
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Digital collage depicting servers extracting water from a local community, symbolizing how data center operations contribute to erosion, water scarcity, and drought.