An anti-cheating flag is a prompt to investigate an interview, not a conclusion about a candidate. The useful question is whether the assessment provides credible evidence of the skills required for the role, under rules the candidate understood.
Our approach combines agreed interview rules, adaptive technical follow-ups, available session signals and human review. The aim is to identify unresolved concerns before a candidate reaches the client's engineers while keeping the recommendation grounded in actual answers.
Define permitted assistance before the interview
An interview that tests independent reasoning needs different instructions from a task that explicitly assesses AI-assisted development. Tell the candidate which tools, documentation and notes are allowed, and explain the interview setup before starting. Establish a way to clarify access needs or technical problems.
For an AI-assisted task, the interesting evidence includes how the candidate checks generated output, recognizes mistakes and explains decisions. Using an allowed assistant is part of that task. Undisclosed assistance in a stage that excludes it presents a different question.
For more on the permitted-use scenario, see assessing AI skills in senior engineers. Do not apply the same interpretation to every stage simply because AI tools exist.
Use the timeline to find the moment worth reviewing
The anti-cheating segment in Recruo's 1:25 demo, starting at approximately 1:05, shows an analysis breakdown and an event timeline. Visible categories include liveness, gaze attention and keyboard activity. The report also displays written explanations beside the signals.
The practical value of a timestamp is that a reviewer can locate the relevant moment and consider it alongside the question and answer. A reviewer should still check that the available evidence supports the interpretation. A risk label is not a validated probability that somebody cheated.
The demo illustrates the interface and workflow. It does not establish a detection accuracy rate. We do not treat it as evidence that every form of outside assistance can be detected.
Separate observation from interpretation
A useful review distinguishes what the system recorded, possible explanations and the next check. Consider these illustrative examples:
| Recorded signal | Question for the reviewer | Possible next step |
|---|---|---|
| Looking away during an answer | Is there context such as permitted notes or an interruption? | Review the moment and ask for clarification if relevant |
| Keyboard activity | Was typing expected for this task? | Check the stage instructions and available session evidence |
| Face temporarily not detected | Was there a camera or connection problem? | Check the recording and technical context |
| A polished but shallow answer | Can the candidate explain the underlying reasoning? | Ask a role-relevant follow-up with a changed constraint |
These examples are review prompts, not rules that assign guilt or innocence. Neither eye direction nor keyboard activity alone establishes who produced an answer. A session with no flags also does not prove that outside assistance was impossible.
Ask a follow-up that tests understanding
When the technical evidence is thin, a concrete follow-up can help. If a candidate names a caching tool, ask how they would handle stale data in a specific scenario. If they describe an index as a lookup optimization, explore its cost when writes increase.
The purpose is to establish depth, including when the initial answer sounded polished. A failed follow-up may reveal a skill gap; it does not by itself prove cheating. Keep the skill assessment and any integrity concern separately documented.
Give the candidate space to correct an answer or explain ambiguity. The full Recruo demo includes an opportunity to update an earlier answer before the interview ends. That is useful context for interpreting the final response rather than treating the first wording as the entire assessment.
Make human review specific
Before a recommendation, the reviewer should check the interview rules, relevant moments, answer-level evidence and any candidate clarification. Record what was observed, what remains uncertain and why a follow-up or further assessment is needed.
Recruo Secure Browser can add controls and session-integrity signals where supported by the agreed setup. Ask which controls apply to your search. Browser controls, behavioral signals and technical questioning each provide different evidence; none makes the interview infallible.
For hiring managers, the outcome should be a clearer basis for the next decision. Recruo's recruiters assess candidates before introduction and review the results. Your engineers then run the interviews that matter for your company, with technical evidence and remaining questions available to them.
See how assessment evidence is handed over and Recruo's assessment workflow.