How to detect AI-assisted candidates in interviews

• The pause-then-essay pattern. A fixed silence after your question, then a fluent, structured mini-essay with textbook transitions — noticeably more polished than the same person’s small talk five minutes earlier.
• Reading cadence. Even pace, falling intonation at the end of each line, almost no mid-sentence self-repair. People retrieving real memories hesitate, backtrack and correct themselves; people reading don’t.
• Gaze anchored to one screen region that re-engages right after each of your questions, with eyes tracking line by line. Glancing at notes looks different: brief, at the start, then back to the camera.
• Latency inversion. Instant, confident answers on obscure technical trivia — then vagueness or delay on “what did you do in that project?” Humans are fast on their own history and slow on trivia; tools are the reverse.
• Keyboard or trackpad sounds in the “thinking” silence right after your question.

None of these is proof, and several have innocent explanations — that matters, and it’s why the next two sections exist.

Never accuse mid-interview. Change the question instead — every verification below is just good interviewing, so it costs an honest candidate nothing:

• Chain into their specifics. Pick a detail from their last answer and go two levels deeper: “you said week four is when it slipped — what exactly broke that week?” A tool only knows what the candidate relays to it, and relaying context takes time you can hear.
• Change a constraint mid-problem. “Good — now the API is rate-limited to ten requests a second. What breaks first?” Scripted answers fracture on live edits.
• Ask them to argue the other side. “Make the strongest case against the design you just proposed.” Real understanding flips perspective easily; coached answers restate the original with negatives bolted on.
• Move to a shared doc. Work one example together in a document or editor you both type in. Collaboration speed is very hard to fake through a relay.
• Probe lived experience. “Who pushed back, and what did they actually say?” “What would your teammate say you got wrong?” Specific, sensory, personal — the territory where a tool has nothing to retrieve.

The overlays are engineered to stay out of screen capture, and the no-software version — a phone beside the screen — beats every scanner by existing outside the machine. Requiring exam-style proctoring for a job interview punishes every honest candidate with surveillance and raises its own legal questions. A small market of detector tools appeared in response to the cheating wave, and it lives in a permanent arms race with what it detects. Use technology as a minor layer if at all; the durable defense is the craft in the previous section, which no software update can obsolete.

Every signal above is also produced by innocent humans. Nervous candidates pause oddly. Non-native speakers take formulation time that looks like latency. Neurodivergent candidates may avoid the camera entirely. Prepared candidates legitimately glance at notes. Interview coaching produces structured answers honestly.

• Signals justify deeper follow-ups, never verdicts.
• Score what the follow-ups reveal — depth collapses fast when answers are being relayed — not the vibe of the first answer.
• Write observed behavior in your notes (“couldn’t expand on the migration detail they introduced”), never an accusation (“was using AI”). Notes get discovered; behavior is defensible, speculation isn’t.

Detection is the second-best tool. The first is designing rounds where assistance has nothing to grab: interview questions an AI can’t answer for them covers the patterns — context chains, artifact walkthroughs, reasoning in motion — with examples, and the role question sets give you the substance to chain from. For the wider picture, start at AI cheating in interviews: what hiring teams are up against.

Follow-up chains only work if you remember exactly what the candidate said. incogno keeps a live transcript of their answers beside your notes, so “what did they say about the rollback?” has an instant answer and your next probe lands on their actual words. It runs on your Mac or Windows machine, no bot joins the call, and the saved summary turns the interview into evidence for your scorecard. See the interviewer’s AI assistant.