
Prepare people for the physical task.
The scan explains movement, framing, and environmental requirements before capture begins.
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.

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

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

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

Specific issue results support self-correction.
Preparation, live scanning, analysis, results, and retry states from the workspace-validation prototype.

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.
More retries may improve capture quality but increase anxiety and time.
The 70% detection and 20% false-positive figures are experiment targets, not achieved production results.
Retest the scanner guidance and retry threshold before making a production efficiency claim.
Includes the role breakdown, evidence, edge cases, outcomes, and lessons.