How to Detect AI Use in Technical Interviews: A Guide for Engineering Leaders

A Practical Guide for Training Interviewers in the Human + AI Era

AI use in technical interviews can be detected by observing candidate behavior, coding patterns, and reasoning during live interviews. Common signals include continuous screen switching, instant fully formed solutions, inconsistent explanations, and pasted code blocks.The most reliable approach is training interviewers to evaluate reasoning, validation, and AI judgment rather than only code output.

Over the past year, one question has come up repeatedly in conversations with engineering leaders: “How do we know if candidates are using AI during our technical interviews?”

It’s an understandable concern. Modern coding assistants can generate working solutions in seconds, and many interview formats were designed for a world where engineers wrote most code manually.

Our research shows the long-term answer isn’t banning AI. The most reliable hiring signals increasingly come from human-led interviews where candidates can use AI while a live interviewer evaluates how they think, reason, and validate code. That’s where the industry is heading with NextGen interviews. And we’ve shared quite a bit of content on how to update talent rubrics to assess AI proficiency and how to train interviewers to assess AI readiness. 

But many organizations aren’t ready for that transition yet.

In the meantime, engineering leaders still need a way to protect interview integrity and detect suspicious behavior when AI or external assistance is being used improperly.

The good news: most misuse is surprisingly easy to detect — if interviewers know what to look for.

Why Live Technical Interviews Still Work in the AI Era

The biggest misconception about AI is that it makes technical interviews impossible to trust.

In reality, live interviews are still the strongest defense against cheating.

Unlike take-home projects or asynchronous coding tests, live interviews allow interviewers to observe how candidates actually work:

• How they break down a problem
• How they write and revise code
• How they debug issues
• How they explain their thinking

These behaviors are extremely difficult to fake when someone is simply copying AI-generated output.

Organizations can further strengthen interview integrity with several safeguards:

• Live human interviewers who can probe deeper when something looks suspicious
• Video-recorded interviews that allow hiring teams to verify candidate identity
• Structured problems that require reasoning, not just working code
• Regularly refreshed interview questions to prevent leaked solutions

But the most important safeguard is interviewer training.

Interviewers need to recognize the behavioral patterns that often indicate a candidate is relying on external assistance.

The Two Most Common Types of Technical Interview Cheating

Based on our experience conducting (and reviewing) more than half a million technical interviews, most instances of cheating fall into two main categories:

1. Candidate Impersonation in Technical Interviews

In this scenario, a candidate has someone else take the interview on their behalf.

Remote hiring has made this easier than in-person interviews, but simple safeguards can mitigate the risk:

• Recording video interviews
• Asking candidates to disable background filters
• Requesting simple verification gestures (such as turning their head or raising a hand)

These checks make it far harder for someone to impersonate a candidate.

2. Unauthorized AI or External Assistance

The more common issue today is with candidates seeking unauthorized help.

This has historically included copying answers from coding platforms or consulting another person (either off-camera or via an earpiece). And today, that also includes using an unauthorized AI tool. 

Because this activity typically happens offscreen, detection relies on recognizing (and recording) suspicious behaviors.

6 Signs a Candidate May Be Using AI During a Technical Interview

Across hundreds of thousands of technical interviews, several patterns consistently appear when candidates are using outside assistance.

Training interviewers to recognize these signals is one of the most effective ways to maintain interview integrity.

1. Frequent Screen Switching or Looking Offscreen

Frequent glancing offscreen or switching windows is one of the most common suspicious behaviors.

This may indicate a candidate is:

• Referencing another monitor
• Copying the problem into another tool
• Using an external device

Other related signals include highlighting the problem text repeatedly or reading prompts aloud to capture them in another system.

2. Writing Perfect Code With No Iteration

Real engineers rarely write perfect code in one pass.

Instead, they typically:

• Start with a rough approach
• Write partial logic
• Iterate and refine their solution
  

Candidates relying on external tools sometimes produce a full solution in a single uninterrupted pass with no visible iteration.

3. Immediate Optimized Solutions Without Reasoning

Another red flag is when a candidate jumps directly to an optimized solution without explaining their reasoning.

This can happen legitimately if they have seen the problem before. But when a perfect solution appears instantly without any discussion of approach or tradeoffs, interviewers should probe deeper.

4. Explanations That Don’t Match the Code

One of the clearest indicators of external assistance occurs when a candidate’s explanation doesn’t match their code.

Examples include:

• Writing correct logic but being unable to explain how it works
• Describing the wrong algorithm
• Providing inconsistent reasoning when asked follow-up questions

This often happens when a candidate copies AI-generated output without fully understanding it. Again, probing and assessing problem-solving techniques can separate genuine understanding from prompted answers.

5. Large Blocks of Code Appearing Instantly

Another signal is when large blocks of code appear instantly in the editor.

Clues may include:

• Entire solutions appearing at once
• Inconsistent indentation styles
• Variable naming conventions that suddenly change

These patterns often indicate code generated or copied outside the interview environment.

6. No Awareness of Edge Cases or Failure Scenarios

Experienced engineers instinctively think about edge cases.

If a candidate claims their solution is correct but cannot explain the following, it may indicate they did not reason through the solution themselves:

• failure scenarios
• boundary conditions
• testing strategies

How to Train Interviewers to Evaluate AI-Assisted Candidates

Bringing your interviewer training program up-to-date for the human + AI era requires more than just integrating an AI tool into your IDE. You need to rethink what interviewers are there to measure, and equip them with the right techniques and tools. This technical interviewer training framework walks you through the steps to do so. 

Step 1: Shift Interview Goals From Code Output to Reasoning

First, you have to redefine what an effective interview measures. Seeing if a candidate can produce a working solution is no longer the goal since it doesn’t generate the same hiring signal that it used to before AI was widely adopted. 

Instead of focusing on memorization and syntax, train interviewers to measure:

• Reasoning: Can the candidate break down the problem, articulate an approach, weigh tradeoffs, and explain why they’re making specific decisions?
• Validation: Can the candidate test and verify their work, including AI-generated code? How do they decide that something is safe to ship?
• Collaboration with AI: How does the candidate decide when to use AI tools, and how effectively does it use them? How do they prompt, iterate on, and challenge results?

Step 2: Teach Interviewers Probing Techniques

Next, train interviewers on probing techniques that surface how candidates think:

• Asking candidates to explain their choices. Have candidates talk through the options they considered and why they chose one path over another.
• Exploring edge cases. Ask candidates to identify potential points of failure or performance concerns, and how they would test them.
• Challenging assumptions. Frame a shift in a key assumption and see how candidates question their original approach, revisit tradeoffs, and update their plan instead of defending their first idea at all costs.

Step 3: Use Structured Human + AI Interview Rubrics

Structured interview rubrics make it easier to train interviewers. They define clear competencies and observable behaviors for the skills that matter most. This allows interviewers to understand what they’re assessing and how to measure it. 

Rubrics that are built for human + AI interviews help:

• Ensure consistency. A shared rubric gives every interviewer the same set of competencies and behaviors to look for, so candidates are evaluated against the same bar regardless of who runs the interview.
• Reduce interviewer bias. When there’s explicit criteria to evaluate candidates against, it shifts the assessment from being based on gut feel to observable evidence. This makes it harder for unconscious bias to drive hiring decisions. 
• Align evaluation with modern engineering work. They help organizations intentionally weight the competencies that lead to success in modern workflows. This ensures interviews are still an accurate predictor of on-the-job performance rather than a test of pre-AI interviewing norms.

Step 4: Simulate Real Engineering Environments

Interviewers can’t evaluate AI-era skills if the interview environment still reflects how engineers worked a decade ago. The best interviews mirror real work. Today, that means the interview environment needs to include:

• Live coding. When candidates work in real time, interviewers can observe how they break down a problem, iterate, debug, and respond to feedback
• Multi-file codebases. Working within realistic, multi-file projects lets interviewers see how candidates navigate existing systems, find the right place to make a change, understand dependencies, and consider the downstream impact. This also surfaces practical skills that are increasingly important with AI tooling such as reading unfamiliar code, building context quickly, and making safe, incremental changes.
• AI-enabled IDEs. Providing access to AI assistants inside the IDE reveals how candidates prompt, validate, and refine AI-generated code. This gives interviewers a direct view into the candidate’s AI usage and how they use real-world tools.

4 Mistakes Companies Make When Training Interviewers for AI Interviews

Many organizations understand that AI is reshaping technical interviews, but their interviewer training programs haven’t caught up. Instead of updating how they define, observe, and score AI‑era skills, they continue to use outdated interview techniques and tools.

The result is a widening gap between the engineers they think they’re hiring and the ones they actually bring in. The four mistakes below show where interviewer training actually undermines the quality and integrity of your hiring signal. 

Mistake #1: Focusing Only on Detecting AI Cheating

Interviewer training that focuses primarily on catching candidates using AI misses the point. The goal isn’t to detect cheating. It’s to produce a reliable signal about a candidate’s ability to do the job. When that job increasingly involves AI, training that solely focuses on cheating detection does little to improve the quality of your hiring signal. 

Interviewers should be trained to accurately and consistently evaluate AI‑assisted engineering competence, rather than assume bad intent. Treat cheating detection as one aspect of your interviewer training and not the core of it. 

Mistake #2: Treating AI Usage as Inherently Bad

The instinct to prohibit AI in interviews is understandable, but increasingly counterproductive as engineers use AI every day. Interviews that ban AI use measure a narrow slice of a candidate’s actual capability and do so in an environment that doesn’t feel like the actual job.

When training frames any AI usage as bad, interviewers tend to penalize candidates for using tools that have become standard in engineering work. This not only pushes strong, AI‑proficient engineers away, it also obscures the very skills that your AI workforce strategy depends on.

Mistake #3: Not Training Interviewers to Evaluate AI Judgment

How engineers choose when and how to rely on AI is now a critical skill, but only 22% of CIOs at financial services companies are training interviewers to assess AI readiness in engineers. Without explicit guidance on what good AI judgment looks like, interviewers don’t have a shared language for evaluating whether a candidate is thoughtfully guiding, challenging, and validating AI outputs or simply deferring to them.

Mistake #4: Using Outdated Technical Interview Rubrics

Many organizations are still scoring interviews with rubrics designed for a pre‑AI world, which focused on syntax and memorization. Without updating rubrics to reflect AI-native competencies, interviewers will evaluate the wrong things and use their gut feeling to assess candidates. 

The Future of Technical Interviews in the Human + AI Era 

As interviews evolve toward AI-enabled environments with live interviewer engagement, interviewer training becomes less about administering a test and more about consistently evaluating real engineering competence in modern workflows.  

Leading organizations are already treating technical interviewer training as part of their AI workforce strategy and redesigning their interviews to reflect the human + AI environments where engineers actually work.

These three shifts define where interviewer training is headed next:

• Interviewers act more like engineering peers. Instead of simply handing over a problem and quietly watching the candidate work, they ask clarifying questions, discuss tradeoffs, and probe the why behind each decision. They collaborate with candidates in AI-enabled environments, similar to the way a teammate would in a pairing session.
• Interviews mirror real workflows. The best formats look more like day-to-day engineering. Candidates work in multi-file repositories, read unfamiliar code, and validate changes instead of solving isolated puzzles that reward memorization. Human-led, AI-enabled interviews allow candidates to use tools the way they would on the job, while live interviewers probe how they navigate ambiguity, reason, and adapt.
• AI literacy becomes a core competency for both candidates and interviewers. Interviewers must understand what good AI usage looks like. This includes how to prompt effectively, when to trust or override AI suggestions, and how to ask follow-up questions that surface a candidate’s AI judgment. Organizations that train interviewers to interpret AI-assisted work, use human + AI interview rubrics, and continuously recalibrate as AI evolves will be best positioned to identify the engineers who can multiply their impact with AI. 

Why Interviewer Training Is Now a Strategic Discipline

AI doesn’t replace interviewers. It raises the bar for them.

In a world where a candidate can produce a working solution in seconds, the goal of the technical interview is no longer to see if they can. It’s to understand how they think and how they use the tools available to them, and that requires interviewers who know what to look for, how to probe for it, and how to score it. 

Structured interviewer training prepares interviewers to do just that. It also protects your hiring signal by making sure interviews remain fair, consistent, and aligned with how engineers actually work today.

When organizations no longer treat interviewing as a transactional task but as a strategic discipline, they’re better equipped to confidently identify top talent. Those that make this investment will win the race for talent because they can identify relevant engineering skills faster and more reliably than organizations where interviewers still follow pre-AI methods. 

FAQs: AI Use in Technical Interviews

What should interviewers evaluate in AI-assisted interviews?

In AI-assisted interviews, interviewers should evaluate four core capabilities beyond coding output: engineering judgment, AI tool usage, systems thinking, and communication. A structured technical interview rubric helps interviewers assess these capabilities consistently.

How do you train interviewers to assess AI skills?

Training interviewers to evaluate AI skills involves four steps: 

1. Shifting the interview goal from code output to reasoning, validation, and collaboration with AI
2. Teaching probing techniques that reveal understanding vs. overreliance
3. Implementing structured human + AI rubrics
4. Simulating real engineering environments

Interviewers also need to be trained to recognize suspicious behaviors, like top-down coding or misaligned explanations, that may indicate overreliance on AI.

Should candidates be allowed to use AI in technical interviews?

Candidates should be allowed to use AI in technical interviews as it produces better hiring outcomes. According to Karat’s research, companies using human + AI interviews are more likely to anticipate reductions in coding errors, faster time-to-market, and increased product or feature development than those using human-only or automated assessments. 

What is a human + AI technical interview rubric?

A human + AI technical interview rubric is a structured evaluation framework that measures the full range of competencies required in an AI-augmented engineering environment. This includes navigating codebases, communication, problem-solving, and AI proficiency. Instead of focusing on whether a candidate solved a problem correctly, human + AI rubrics evaluate how they solved it and whether they could do so reliably in a production context.

How can interviewers detect AI overreliance?

Interviewer training for the AI era equips interviewers to notice specific behavioral patterns that may indicate overreliance, such as continuous look-aways or window-switching, top-down coding, and explanations that don’t match the code. Interviewers also ask candidates to explain their choices, justify their approach, and walk through edge cases. Genuine understanding holds up under questioning while overreliance usually doesn’t.