Case Study
Designing structured AI-supported feedback and learner tracking systems to improve consistency, reduce repetitive administration, and make technical education more scalable.
Technical education creates a high volume of repeated feedback, learner guidance, assessment support, and administrative tracking. Without a structured system, feedback can become inconsistent, time-consuming, and difficult to scale across multiple learners and cohorts.
Learners also need clear, accessible explanations that show exactly what is correct, what needs improvement, and what their next step should be.
I developed structured feedback workflows using AI-assisted drafting, reusable feedback formats, automated checking approaches, and learner tracking systems.
The aim was not to replace professional judgement, but to support faster, clearer, more consistent feedback while keeping the trainer in control of the final review.
Created consistent feedback formats for learner submissions, making responses clearer, easier to understand, and easier to align with assessment expectations.
Used prompt engineering to help generate first-draft feedback that could then be reviewed, corrected, personalised, and approved by the instructor.
Developed code checking approaches to help identify common issues in learner work, support consistency, and reduce repetitive manual review.
Built structured tracking approaches to monitor progress, highlight support needs, and improve visibility across delivery and quality processes.
Mapped repeated teaching and assessment tasks into clear workflow stages, including submission review, issue identification, feedback drafting, trainer review, and learner follow-up.
Designed reusable AI prompts that supported consistent structure, accessible language, and assessment-aligned feedback while avoiding unsupported assumptions.
Applied automation principles to reduce manual repetition and support scalable learner communication, while keeping sensitive decisions and final judgement human-led.
Focused on clear wording, learner-friendly explanations, SEND-aware support, and feedback formats that could be understood by mixed-ability and ESOL learners.
Feedback became more standardised across learners, making expectations clearer and reducing variation in tone, depth, and structure.
Reusable workflows reduced the amount of repeated drafting needed for common learner issues and recurring assessment problems.
Learners received clearer next steps, simpler explanations, and more structured support for improving their work.
The system supported evidence-based delivery, clearer feedback records, and more reliable assessment support.
The next stage would be to develop this into a more formal web-based platform with secure user access, dashboard reporting, learner progress analytics, reusable feedback libraries, and optional OpenAI API integration for controlled AI-supported drafting.