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AI-Guided Environment Validation·Computer vision · bounded human-AI recovery

First-attempt failures showed that recovery needed as much design attention as scanning.

I designed and tested a guided mobile scan that could detect room issues, explain what needed correction, bound repeated failure, and guarantee a route to a human greeter.

AI-guided workspace scan preparation and live capture sequence
Hero proof · Validated proof of concept
// RoleProduct Designer · UX/UI + validation
// AudienceRemote exam candidates · greeters
// TeamProduct · computer vision · engineering · operations
// TimeframeProof of concept · six-participant study
// MaturityValidated proof of concept
// Signal3.8/5 overall · 4.1/5 issue results
// Three product decisionsInterface first

Three decisions shaped scanning and recovery.

Replace four static room photos with guided video and issue detection without allowing persistent AI uncertainty to block an exam.

Workspace scan candidate, AI, and human-review flow
Prepare · explain the task before capture.
Decision 01

Prepare people for the physical task.

The scan explains movement, framing, and environmental requirements before capture begins.

Live workspace scanner guidance and retry states
Guide · keep correction close to the action.
Decision 02

Give guidance while the candidate can still correct course.

In-scan feedback makes framing and coverage visible instead of waiting until the end to announce failure.

Workspace scan analysis, issue explanation, and completion states
Recover · explain, retry, or hand off.
Decision 03

Bound retry and preserve a human exit.

Specific issue results support self-correction.

// UI evidenceAdditional product evidence

More of the guided scan experience

Preparation, live scanning, analysis, results, and retry states from the workspace-validation prototype.

Product walkthrough
Earlier four-photo workspace capture approach before guided scanning
Earlier state · baseline before guided scanning
Evidence and outcome

Participants struggled most with the scanner.

Six participants rated issue results 4.1/5 and the overall proof of concept 3.8/5, but the scanner itself scored 3.4/5. None succeeded on the first scan, and participants restarted one to four times.

// ReflectionLimits and next questions

The proof of concept exposed what still needed work.

Tradeoff

Tradeoff

More retries may improve capture quality but increase anxiety and time.

Limitation

Evidence boundary

The 70% detection and 20% false-positive figures are experiment targets, not achieved production results.

Next step

Next test

Retest the scanner guidance and retry threshold before making a production efficiency claim.

// Protected deep caseDecisions · evidence · edge cases

See the evidence behind the decisions.

Includes the role breakdown, evidence, edge cases, outcomes, and lessons.

// Talk about a complex product

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