Explore the best ways recruiters can detect and prevent unethical use of ChatGPT in coding interviews.
Abhishek Kaushik
Oct 30, 2025
A quiet shift has happened in hiring.
ChatGPT and similar large language models have rewritten what "preparation" means for candidates.
Developers no longer walk into interviews alone, they walk in with a silent partner capable of generating perfect code, clean syntax, and flawless explanations.
In a world where 1 out of 5 employees admits using AI during interviews, pretending this isn’t happening is naïve.
AI is changing the very nature of technical evaluation.
Recruiters aren’t just assessing people anymore - they’re assessing people assisted by AI.
The real question isn’t whether AI belongs in interviews — it’s already here.
The question is: are we still measuring skill, or just the art of prompting?
"Student used AI to beat Amazon’s interview” photo from Gizmodo
Why This Becomes a Problem
When AI enters interviews without disclosure, three things break at once:
1. The Signal Becomes Noisy
The primary purpose of a technical interview is to assess predictive validity - how well a candidate’s problem-solving behavior correlates with future job performance.
When candidates use generative AI (e.g., ChatGPT) to produce solutions, hiring managers lose visibility into key behavioral indicators: cognitive process, debugging strategy, and adaptive reasoning under pressure.
The result? The assessment loses its fidelity as a predictor of on-the-job success.
2. The Cost of Mis-Hire Rises
Recruiters and hiring managers often assume strong interview performance reflects true capability.
However, candidates who rely heavily on AI assistance can exhibit a performance gap post-hire, struggling with autonomy, ambiguity, and technical depth.
According to SHRM, the average cost of a single bad engineering hire exceeds $240,000, excluding downstream impacts such as reduced team productivity, manager burnout, and loss of trust in the selection process.
This inflates the Cost per Hire and drives up Quality of Hire variability - key metrics in modern talent acquisition.
3. The Culture Erodes
Perceived inequity in the hiring process corrodes employer brand integrity.
If engineers believe peers are “AI-cheating” their way through interviews, engagement and morale suffer.
Ethical consistency is not merely a compliance matter - it’s a cultural signal.
Once fairness is questioned, even high-performing candidates begin to doubt the legitimacy of the process, damaging candidate experience and long-term talent retention.
Current Fixes Don’t Work
Most proctoring and assessment-monitoring tools address surface-level behaviors - tracking browser activity, tab switching, or webcam presence.
These methods fail to capture behavioral authenticity - the subtle rhythm, hesitation, and problem-solving cadence that distinguish genuine human reasoning.
While some organizations attempt to ban AI tools outright (alienating progressive, tech-forward talent), others take a laissez-faire approach (allowing undetected misuse to proliferate).
Both strategies are operationally unsustainable and strategically shortsighted.
A Smarter Way Forward
The goal isn’t to stop AI.
It’s to understand when it’s helping and when it’s replacing.
That’s why we built Sherlock - an AI agent that detects AI use not by blocking it, but by analyzing the process behind it.
Here’s how it resolves the core issues:
Problem | Sherlock’s Approach | Outcome |
|---|---|---|
Hidden ChatGPT use | Behavioral forensics (typing rhythm, code stylometry, tab pattern) | Transparent visibility into AI assistance |
High mis-hire risk | Integrity scoring across interviews | Reliable skill signals and predictive accuracy |
Eroded trust | Clear, explainable AI detection | Restored confidence for recruiters and candidates |
Sherlock brings clarity, not punishment.
It quantifies how much of a response stems from human problem-solving versus AI generation, enabling talent teams to make informed, context-driven hiring decisions:
12 non-negotiable criterias to look for before selecting a solution to solve AI fraud problem in Interviews
Accept partial AI use for ideation and efficiency
Flag full AI substitution for further review
Adapt interview frameworks based on candidate behavior
The outcome is straightforward:
Fairer evaluations. More predictable hires. Transparent AI use - not hidden dependency.
Final Thought
We don’t detect ChatGPT because we fear it.
We detect it because trust in hiring depends on knowing what’s human and what’s not.
As AI becomes a permanent co-pilot in work, companies that instrument for authenticity - not just performance - will win.
Sherlock just makes that authenticity measurable.
👉 Discover how Sherlock detects AI use with forensic precision → withsherlock.ai
