You reduce AI cheating on technical assessments mainly by changing the test, not by buying a better detector. Assume candidates have an AI model open, test reasoning and debugging rather than recall, then add a short live follow-up where they explain and modify their own submission. Inconsistency across stages shows up on its own.
If an assessment can be solved by pasting the prompt into a chat window, it was probably measuring the wrong thing before AI arrived.
Why does detection struggle?
Webcam proctoring and lockdown browsers watch the candidate and their screen. AI help can come from a phone or a second machine the proctor cannot see. Paste and keystroke analysis catches a 400-line paste and misses anyone who retypes the output.
Detection also has a false-positive cost. Accusing an honest candidate who types fast and uses snippets does more damage than letting one weak candidate through to a live round. Use detection as a signal for a person to review, not as a verdict.
What does an AI-resistant assessment look like?
Design for the model being in the room. Your engineers use these tools every day, so testing whether a candidate can code without them tests a job that no longer exists.
"Implement a binary search tree" is easy for a model. "Here is working code with a subtle concurrency bug and a failing test. Find it, explain it, fix it, and tell me what else this pattern would break" is harder, because the candidate has to reason about the trade-off out loud a minute later.
Why is a live follow-up so effective?
Because it checks understanding of work already submitted. Ten minutes is enough: ask why they chose that data structure, what happens under 10x load, and ask them to change one requirement and edit the code in front of you. Candidates who wrote it usually answer fluently.
GetHirePlus live coding interviews include a built-in code editor with recording and proctoring, so the follow-up happens in the same tool as the assessment.
Which formats hold up better?
Multiple-choice knowledge checks and standard algorithm puzzles are easy to answer with AI help. These hold up better: timed reasoning under constraints, open-ended problems where the candidate must decide the requirements first, and questions tied to your product or codebase, where a generic answer looks generic.
GetHirePlus candidate assessments support MCQ, coding with a live compiler, puzzles, open-ended and subjective formats, all proctored, so you can combine several formats instead of relying on one.
Why use several stages instead of one gate?
If one take-home decides who advances, gaming it pays off. If the candidate must be consistent across a screening conversation, an assessment and a live round, gaps appear without anyone making an accusation.
A practical stack: an AI resume screen for relevance, a short AI phone interview with a transcript and scores, one proctored assessment focused on reasoning, then a live round where the candidate walks through their own work.
Where is the compliance line?
If an automated tool screens, ranks or rejects candidates, rules such as New York City's Local Law 144 and Illinois' HB 3773 add audit and notice duties. A cheating score that silently rejects people is exactly the kind of automated decision that draws scrutiny. Keep a person in the loop on rejections.
Can you get cheating to zero?
No. Some candidates will pass an assessment they did not earn. That is acceptable if a false pass costs one wasted interview rather than one bad hire. The goal is a process where cheating stops being worth the effort well before an offer goes out.