Why this matters

Generative AI and online resources are now part of many candidates’ workflows. That’s not always a problem—but when the goal is to evaluate their skills under realistic constraints, silent assistance can skew results. Reading Behavior Analysis helps teams:

• Spot likely off-screen assistance (e.g., a second monitor, tablet, or phone) even when no on-device plugins are involved.
• Triage submissions faster with structured, time-stamped evidence.
• Protect fairness for candidates who play by the rules.

What reviewers see

• Short summaries (e.g., “Reading pattern detected for ~18s before answer to Q2”).
• Contextual overlays with the relevant metrics (eye path stability, head pose variance, relative speed signals) so reviewers can quickly judge severity and intent.
• Consolidated report across the full session to understand whether signals are isolated blips or a recurring pattern.

AI usage detection—what we claim (and don’t)

Reading Behavior Analysis is not a polygraph or a definitive AI detector. Instead, it flags behavior consistent with reading from an external source while answering. That can include:

• A second monitor with AI tools or web content
• A phone or tablet just off camera
• Printed notes or another screen placed beside the camera

If it matches a reading pattern, Feenyx proactively surfaces it for review—regardless of device type.

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FAQs

Does this affect candidate experience?
No changes to the recording flow. Analysis runs post-upload.

What about false positives?
Reviewers remain the arbiter. You can mark and exclude segments, adjust sensitivity, and whitelist allowed behaviors (e.g., reading problem statements on-screen).

Can it catch AI usage for sure?
It flags reading-like behavior strongly associated with using external content—including AI tools—but it’s not a definitive AI detector. Treat it as high-signal evidence, not a verdict.

What data is stored?
We retain the original video (per your retention policy) and derived motion metrics tied to the session for audit and model improvement within Feenyx’s privacy framework.

Is this inclusive for candidates with different needs?
Yes. We support policy exceptions and reviewer notes. If your process allows accommodations (e.g., reading aids), you can reflect that in policy and reporting.

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