Case study 04 · Evaluation & quality assurance

A Learning Quality Scorecard for AI Programmes

A practical way to tell the difference between a programme that was delivered and one where students actually learned.

Independent evaluation modelDemo dashboard uses synthetic data only
  1. ProblemDone
  2. DesignDone
  3. BuildNot yet
  4. ReviewNot yet
  5. ReviseNot yet

The challenge

Completion rates and happy-face surveys are nice, but they don’t show whether students can reason about AI, check its answers or use their skills somewhere new. Leaders need clear signals that link teaching quality, learning progress, risks and next steps.

My design response

The scorecard groups measures into four areas: how students reason, curriculum quality, teacher readiness and how reliably the programme runs. Each measure has a definition, a data source, how often it’s reviewed, who owns it and a decision rule, so collecting data actually leads to action.

Sample measurement model

DimensionExample indicatorMethod / caveat
Learner reasoning% meeting “Secure” on verification rubricAssess with common tasks; report sample size
Learning progressionChange in rubric level across checkpointsSame criteria; avoid claims without comparability
Content quality% of lessons passing age/accuracy/safety QAUse documented review checklist
Educator readiness% demonstrating skill in scenario-based assessmentObservation or scenario task, not confidence alone
Delivery reliabilityOn-time delivery and critical issue resolutionTrack issue severity and exclusions
Feedback to action% improvement actions closed by due dateLink actions to owner and evidence

Example decision log

What we notice: Students hand in polished AI answers but hardly ever cite sources. Why it might happen: the assessment rewards the final product; checking sources isn’t modelled consistently; prompts don’t ask for evidence. What we try: add a check-and-cite routine, update the rubric and review a sample of answers at the next checkpoint.

This is a made-up example to show the design, not a description of any real employer programme.

Sample dashboard

Synthetic demonstration data
An illustrative dashboard. No figure here describes a real programme, learner or organisation.
58%Learner reasoningMeeting “Secure” on the verification rubric
91%Curriculum qualityLessons passing age, accuracy and safety review
72%Educator readinessDemonstrating skill in a scenario task
94%Delivery reliabilitySessions delivered on time

Learners meeting “Secure” on verification, by checkpoint

0% 50% 100% CP1 CP2 CP3 CP4 58%

Open issues

  • High: polished outputs without cited sources
  • Medium: rubric applied unevenly across groups
  • Low: one review checklist out of date

Action tracker

ActionOwnerEvidence requiredState
Introduce a check-and-cite routineCurriculum leadSample of responses at next checkpointIn progress
Revise the verification rubricAssessment leadUpdated rubric, moderation notesDone
Run a moderation sessionProgramme managerAgreement across reviewersNot started
Read the data as a table, with metric definitions
MeasureSynthetic valueDefinition
Learner reasoning58%Meeting “Secure” on the verification rubric
Curriculum quality91%Lessons passing age, accuracy and safety review
Educator readiness72%Demonstrating skill in a scenario task
Delivery reliability94%Sessions delivered on time
Verification rubric, Checkpoint 131%Share of learners meeting “Secure”
Verification rubric, Checkpoint 238%Share of learners meeting “Secure”
Verification rubric, Checkpoint 349%Share of learners meeting “Secure”
Verification rubric, Checkpoint 458%Share of learners meeting “Secure”

Thinking about how to evaluate a learning programme?