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Artificial Intelligence

AI Evaluation Drift Response Lab

A classroom-ready lab that asks students to investigate changing AI evaluation results, identify release risk, and design a safe CI/CD response plan.
Level
Advanced
Format
Toolkit
Published
May 8, 2026
Artificial Intelligence: AI Evaluation Drift Response LabAn AI lifecycle for AI Evaluation Drift Response Lab, showing data, model evaluation, and human release review.DATAartificial-intelligenceEVALUATEevaluationREVIEWci/cd
Artificial IntelligenceEvaluation lifecycle
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Read the lesson and connect each concept to the visual model above.

This lab turns AI model evaluation drift into a practical release-governance exercise. Students compare two evaluation snapshots, decide whether a deployment should continue, and document the evidence needed before automation can safely resume.

What students can learn

  • How small evaluation changes can create real user-facing release risk.
  • How to separate model-quality signals from pipeline, dataset, or prompt changes.
  • How CI/CD gates should react when AI behaviour becomes unstable.
  • How to communicate an evidence-based rollback, hold, or canary decision.
  1. Give each group the scenario and ask them to classify the release risk within 10 minutes.
  2. Ask groups to map each suspected cause to one verification step and one owner.
  3. Compare proposed CI/CD gate changes, then discuss which controls are useful without over-blocking future releases.
  4. Close with a short incident memo: decision, evidence, remaining uncertainty, and next release condition.

Extension activity

Ask students to convert the response plan into a lightweight GitHub Actions checklist: evaluation threshold, artifact retention, approval note, rollback trigger, and post-release observation window.