How to Detect AI in Coding Interviews

Why code review after the fact does not settle it

Experienced interviewers often sense that a solution arrived too cleanly, but style-based judgment is not evidence. Strong engineers write idiomatic code quickly, and assisted candidates edit output to look ordinary. Deciding on gut feel produces both unfair rejections and quiet false negatives.

Live process observation changes the question from how the code looks to what was running while it was written, which is a question with an answerable, timestamped answer.

Why browser-based tools cannot see the assistance

Browser extensions and lockdown browsers observe the page, the tab, and often the webcam. Their visibility ends where the browser process ends, and that is precisely where modern AI assistance lives.

Desktop assistants and answer overlays render above every other window, are frequently excluded from screen capture and screen sharing, and never interact with the assessment tab. Remote-control tools, virtual machines, injected keyboard input, and a second device off camera are equally invisible from inside the browser.

• Native overlay applications draw over the assessment and are often excluded from screen share.
• Remote-access tools allow another person to drive the machine with no browser-visible trace.
• Virtual machines let the assessment run inside a controlled environment with helpers outside it.
• Injected or synthesized keyboard input can enter answers that were never physically typed.
• A second phone, tablet, or monitor off camera leaves nothing at all in the browser.

What device-level detection observes

ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.

Signals that matter specifically in technical interviews

ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.

Running a fair process

Apply the same monitoring and the same review standard to every candidate for a role, disclose what is monitored before the session, and treat a flag as the start of a review rather than a verdict. Where a finding is ambiguous, a verified re-interview is usually a better outcome for both sides than a silent rejection.

Turning signals into a report someone can defend

ScreenComply supports trustworthy results by reviewing the whole session and flagging concerns for human review, with evidence attached.

Language is deliberately factual rather than accusatory, because the institution or employer makes the decision, not the software. Reports export to PDF and can be shared through a secure link for academic-integrity hearings, HR reviews, and compliance audits, with configurable retention including zero-retention operating modes.