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

Reliable detection has to happen at the layer where assistance runs: the operating system. ScreenComply pairs a browser-based API with an optional desktop agent that inspects the environment around the assessment rather than only the page inside it.

The agent enumerates running processes and browser extensions against a maintained list of AI assistants and answer overlays, looks for always-on-top or transparent windows, detects remote access and virtualization, enumerates connected displays from the operating system, verifies that keyboard input originates from local physical hardware, and records focus changes, clipboard events, and typing-cadence patterns.

No detection system catches everything, and ScreenComply does not claim otherwise. The objective is corroborating, timestamped evidence that lets a human reviewer make an informed and defensible decision.

• Process and extension enumeration against a maintained AI-assistant list.
• Overlay, transparency, and always-on-top window detection.
• Remote access, virtual machine, and screen-capture driver detection.
• Display enumeration reported by the operating system, not inferred from gaze alone.
• Input-source integrity, clipboard events, and typing-cadence analysis.

Signals that matter specifically in technical interviews

Coding interviews have their own signal profile. Editor and IDE extensions matter as much as standalone applications, and input behaviour is unusually informative because writing code has a distinctive cadence compared with transcribing a generated answer.

• Copilot-style editor and IDE extensions active during the assessment window.
• Answer-overlay applications drawn above the editor, including windows excluded from screen share.
• Large code insertions arriving as a single burst rather than as composition and revision.
• Focus loss to another application immediately before a solution appears.
• Remote-control sessions, virtualization, or input that did not originate from local hardware.
• Undeclared additional displays reported by the operating system.

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

A detection is only useful if it can be explained to a person who was not in the room. Every session produces a structured integrity report: an executive verdict with a graded risk level, a chronological evidence timeline, and the underlying signal detail behind each entry.

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.