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TeacherMatic Case Study

University of London

MicroCredential courses assessments
Kirsty Branch, Learning Technologist (Digital Assessment) from the University of London
November 2025

Background
Microcredentials are short, forcused accredited courses designed to help learners develop specific knowledge or professional skills. They are fully self-paced to allow students to study flexibly whilst working. Each microcredential includes a summative assessment, which ensures learners demonstrate mastery of the targeted skills. Microcredential assessment is through a scenario-based coursework task, encouraging students to apply skills to a realised work-based problem or situation. Upon enrolment students have access to their assessment brief and are encouraged to build their responses iteratively throughout the course. As part of the assessment design process, I have explored two different approaches of generating and/or improving the assessment rubrics for these assignments.


Process
I generated the assessment brief using core course information e.g. learning outcomes, weekly summaries, key readings, etc. I used ChatGPT to generate a scenario, role, task, deliverables and rubrics for the brief. To compare the quality and efficiency, I also decided to use TeacherMatic to create the academic rubrics for marking of the assignments.

1. Process was very simple on TeacherMatic: add the brief, some further details e.g. level of study and hit ‘generate’:
A screenshot of the Rubric Generator form
2. ​Useful filing system for any resources generated on the platform:
Screenshot of the file management system
3. Easy to export:
Screenshot to show the requirements for exporting at output from TeacherMatic
Recommendations: 
  1. Gather feedback from markers and students Pilot the TeacherMatic-generated rubrics and actively gather feedback from markers on usability and from students on clarity. This will provide evidence for scaling the approach across the programme.
  2. Maintain human oversight Regardless of AI tool quality, ensure that academic staff always review, adapt, and validate AI-generated rubrics to preserve academic standards and ensure alignment with institutional expectations.

Conclusion
Whilst the rubrics produced by ChatGPT appeared to meet the course Learning Outcomes, they were also vague and therefore not supporting the marking process. In comparison the TeacherMatic rubrics provide clear, specific and actionable rubric content to ensure consistent grading. In addition, TeacherMatic’s streamlined workflow made rubric generation noticeably faster and required fewer refinement steps, making it the more time-efficient option.
Picture
​Photos around the site courtesy of Kit Logan, UCL IOE, Anne Koerber, LSHTM, Tom Graham and James Brown, Birkbeck
Website by Architela
  • Home
  • About
    • Team
    • News
    • History
  • BLE Community
    • Events
    • CMALT
    • Special Interest Groups >
      • Academic + Research Develpoment
      • Assessment & Feedback
      • Climate Justice
      • Digital Accessibility
      • Digital Education
      • Digital Media
      • Distance Learning Administration Network
      • Library Services
      • Student Study Skills
      • Students as Change Makers
      • Systems Thinking
  • Projects
    • Courses >
      • PhD MOOC
      • Digital Skills Courses
      • GetInMooc
    • Jisc Pilot Project >
      • Birkbeck Case Study
      • UoL Case study
    • Research/Publications >
      • Assessment & Technology Ebook
  • Media Lab
    • Expertise >
      • Acting
      • Animation
      • Inclusive practice
      • Copywriting & Copyediting
      • Digital Learning
      • Photography
      • Training
      • Video
    • Spaces
  • Contact