Comparison
AI support vs human judgement in course evaluation
A two-column comparison for quality assurance leads and teaching committees deciding how far to use AI in course evaluation. The left column lists evidence work AI can support, such as sorting and translating student comments. The right column lists judgements that should stay with accountable people, such as decisions about staff and programmes. It follows the line drawn by ENQA's 2025 guidelines on AI in external quality assurance and FIBAA's 2026 AI policy. Koji works on the support side, grouping student interview answers into themes for staff to read and act on.
What's in this comparison
What AI can support
- Group thousands of comments into themes
- Flag courses whose feedback changed sharply
- Translate answers across languages
- Draft summaries for staff to check
What people decide
- Judge whether someone teaches well
- Decide promotion, contract or review outcomes
- Choose what a programme changes
- Sign off what is reported back to students
Use this image
Free to use in your docs, decks and posts under CC BY 4.0, as long as you credit Koji with a link to this page. To embed it, paste this into your page:
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<a href="https://www.koji.so/visuals/ai-support-vs-human-judgement-course-evaluation"><img src="https://www.koji.so/visuals/ai-support-vs-human-judgement-course-evaluation/ai-support-vs-human-judgement-course-evaluation.png" alt="Comparison of what AI can support in course evaluation, such as grouping comments into themes, and what people should decide, such as judging teaching." width="1200" height="641" /></a>
<p>Source: <a href="https://www.koji.so/visuals/ai-support-vs-human-judgement-course-evaluation">AI support vs human judgement in course evaluation</a>, Koji</p>
