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MasarUX
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aipd-a01MasarUX ProAdvanced

Evaluating AI Experiences: Quality, Trust, Safety, and Metrics

Learn to evaluate AI experiences with AI-specific operational methods that conventional usability metrics do not fully capture. Build output-quality rubrics, measure trust calibration, overreliance, and hallucination recovery, design red-team UX journeys and scenario libraries, classify severity, set human-review and escalation thresholds, and assemble decision-ready evidence for a go / no-go / conditional-go launch gate.

20 hours (approximately)3 Levels · 30 Lessons
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By the end of this Course, you can build an output-quality rubric, measure trust calibration and correction effort, design red-team UX journeys and scenario libraries, classify severity, set human-review thresholds, and produce a decision-ready evaluation evidence package for a launch-gate recommendation.

01Define task-level success and build a usable, separated output-quality rubric with acceptable-variability boundaries
02Measure correction effort, false acceptance, and the gap between user confidence and actual quality
03Measure trust calibration, overreliance, and underreliance as observable, actionable behavior
04Evaluate hallucination recovery, refusal UX, and unsafe compliance / boundary behavior
05Build scenario libraries, edge-case scenarios, and UX-level red-team journeys without cybersecurity exploitation
06Evaluate bias, fairness, and accessibility of generated output at the product-experience level
07Classify severity, set human-review and escalation thresholds, and define explicit release criteria
08Assemble a decision-ready evaluation evidence package and communicate uncertainty for a go / no-go / conditional-go launch gate
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Curriculum

3 Levels · 30 Lessons

Assessment

Each Lesson may include a Quiz

Each Level may include an Exam

Completion requirements

Complete the available Lessons

Pass all configured Quizzes and Exams

Locked activities open only after their prerequisites are met

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Course Skills6 Course Skills
Accessibility Evaluation of Generated Output
AI Output-Quality Rubric Design
Evaluation Evidence Communication
Launch-Gate and Severity Design
Red-Team UX Scenario Design
Trust Calibration and Overreliance Measurement
Related Competency Domains
Testing & Validation
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Career Path relationship
AI Product DesignerAgentic Experience Designer

Completed Course progress automatically counts toward the Career Path when this Course belongs to a Mission.

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What You'll Gain
  • Builds one practiced evaluation judgment across 30 bilingual Lessons
  • Keeps every quality claim tied to real, scenario-based evidence, never an unsupported headline number
  • Separates what a launch-gate decision actually supports from what a convincing cherry-picked demo would prefer to imply, at every step
  • Uses scenario-based assessments to rehearse the real judgment calls of a working AI Product Designer's evaluation practice
Who Is This Course For?
  • UX/Product Designers who need to decide whether an AI experience is good enough, safe enough, and understandable enough to launch
  • Designers building output-quality rubrics, scenario libraries, and red-team UX journeys for AI features
  • Product teams responsible for trust, safety, and accessibility evaluation ahead of an AI launch decision
  • Anyone who must turn AI evaluation evidence into a clear, decision-ready launch-gate recommendation
Course Features
  • 3 progressive Levels and 30 substantive bilingual Lessons
  • 30 Lesson Quizzes with 10 scenario-based questions each
  • 3 Level Exams with 15 transfer questions each
  • One structurally validated Practice Task producing a complete AI Experience Evaluation and Launch-Gate Review
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Course Completion Certificate

Awarded after the Course's configured completion requirements are met.