AI services ready
DW
Step 6 · Learn and improve

Learning and engagement insights

Combine student outputs, rapid feedback and lecturer observations to show what worked, where friction occurred and how the next version should improve.

Outcome attainment+21%
88%
Compared with prior tutorial format
Active participation+27%
92%
Students contributing evidence or reasoning
Activity completion5 teams
100%
All required outputs submitted
Lecturer confidenceHigh
4.7
Out of 5 after facilitation

Learning evidence by team

Current activity compared with the previous theory-led tutorial

CurrentPrevious
050100
Alpha
Beta
Gamma
Delta
Epsilon

Student sentiment

“This helped me understand the differences”

25 responses
Strongly agree / agreeOther
“The clues made us argue about why an answer was correct, not just memorise the table.”
Anonymous student feedback

What worked

Signals to preserve

3 strengths
Authentic outbreak context
High impact
Explicit team roles
Balanced voice
Rejected alternative requirement
Deeper reasoning

Observed friction

Signals to address

2 issues
Two teams overused one clue
Prompt needed
Presentation phase ran 4 min over
Timing

AI improvement proposal

Ready for lecturer approval

Version 2

Recommended refinement

Add a “confidence meter” after each evidence card and cap presentations at 5 minutes using a one-slide evidence template.

Continuous improvement loop

Every implementation creates evidence for the next activity design.

Implement activityFacilitation data
Capture feedbackStudents + lecturer
Analyse evidenceOutcomes + friction
Improve next designHuman-approved changes