How Effective Is Your Cheating Prevention Strategy? A Self-Assessment Guide

How do you tackle cheating in technical interviews? How should you tackle it? Bring managers and recruiters together and work through these four questions, to evaluate your current cheating prevention measures and start mapping a strategy that meets everyone’s needs – recruiters, managers, and candidates.

You thought Joe was great. You were excited for him to start. But within a week, it’s abundantly clear that Joe can’t actually… code. 

The team’s livid. Morale has plummeted. That big project gets delayed because everyone’s fixing stuff Joe snarled up. Eventually HR steps in… and it’s back to the job boards.

But you’ve learned your lessons. You load-up with proctoring; copy-paste blocking; IP tracking; screen lockdowns – across every role, region, and geography. And you make questions harder and harder. Ha! Cast-iron. 

Except now, completion rates plummet. Candidate feedback plummets. Application volume plummets. Your Glassdoor profile fills up with disgruntled reviews warning developers not to waste their time. 

Sure, cheating seems like a smaller problem. But hiring is a bigger one. 

That’s the battle many organizations face when hiring technical talent. So how do you tread the line effectively? What’s the right level of mitigation, to tackle cheating without derailing the candidate experience? 

The answer depends on your organization. Work through our four-question self-assessment guide below, to spark discussion towards calibrating a perfect-for-you cheating prevention strategy.

Four questions to guide your cheating prevention strategy

❓ What does “cheating” mean for your organization? 1. We haven’t defined cheating 2. We let our engineering managers decide case-by-case 3. We have a very strict policy. No AI. No copy-and-paste.  4. _______________________

Cheating might seem an obvious concept but it’s nuanced. Is using AI to create a resume cheating or smart? What about using Copilot to generate ideas and editing code from there? Cheating or efficient?

To decide the right level of cheating prevention for your organization, you need to know what you’re trying to prevent. Otherwise you’ll lean towards blanket catch-all solutions that hurt more than help. 

It’s like walling-up a window to prevent mosquitos. Sure, it might work. But it’s overkill. And there are major downsides. 

Think about the balance you want to strike between manager control and central policy, too. It’ll be different for every organization but mostly, disempowering managers with catch-all restrictions is counterintuitive. 

They’re the people on the frontline who know best who’ll excel. And they’re unlikely to take kindly to universal policies that hurt their ability to hire the best people. 

That said though, some degree of coordinated central policy acts as a guardrail. To keep your process consistent and fair, and give managers support to make good decisions. 

Could the organization choose tools that allow each manager to opt-in for different cheating prevention functionality depending on their needs, for instance? 

❓ How much cheating actually happens in your process? 1. We suspect cheating happens but aren’t certain 2. We’ve found some candidates cheating 3. We frequently find candidates cheating 4. _______________________

If you haven’t actually found anyone cheating, could you be overestimating the scale of the problem?

There’s a heap of pearl-clutching about GenAI and cheating. But the plural of anecdote isn’t data. You need more evidence, to guide a response that’s proportionate. Not panicked. 

Think about how you identify possible cheating. If you write-off candidates immediately on the basis of suspicious behaviors, you risk missing great developers. Suspicious behavior is a call to dig deeper into a candidate’s approach, not to blacklist. 

That’s why CoderPad Screen includes AI-generated and validated follow-up questions, to check candidates’ understanding of code. And CoderPad Interview’s code playback functionality means interviewers can replay the entire interview, to see how candidates wrote code and spot areas worth further questions.  

And review your cheat rates regularly. Preventing cheating isn’t a one-and-done, because the problem morphs over time. 

Consider why cheating happens. In a high-competition job market, you’re probably experiencing more cheating, for example. Even great candidates might believe cheating’s the only way to gain a much-needed edge. And the problem starts to snowball as cheating’s normalized. 

Ouch ☝️

But don’t get caught in the same trap. Getting a handle on cheating can feel like chasing someone downhill, sprinting faster and faster. But you’ll eventually lose control – and slowing or changing direction becomes impossible. 

Candidate behavior is fickle. Successful hiring demands a nimble, agile, responsive strategy. 

❓ Where does cheating happen? 1. We don’t know but we’re worried 2. Nowhere: we have cast-iron control over our processes 3. We’ve spotted some specific areas where cheating happens 4. _______________________

There are heaps of touchpoints where cheating *might* happen during hiring. But effective cheating mitigation demands a sniper, not a scattergun. The aim is to avoid collateral damage.

If you’re worried about cheating but there’s no concrete evidence, preventative measures are your best bet. For example, CoderPad’s platform has plenty of features designed to stop cheating before it starts – without hurting the candidate experience with rigid, hardline measures. 

Like:

• Question randomization to mitigate the risk of question sharing
• Question timers to prevent unreasonable searching for answers
• AI content testing to regularly update, test, and safeguard your content 
• Easy-build custom-code exercises to create more cheat-proof tests 
• Gamified coding exercises that are much harder to submit to AI 

But that’s not the only puzzle piece. Preventing cheating is also about building a realistic, engaging, respectful process that adds value for candidates – so they’re far less inclined to cheat in the first place.

But what if you’re facing a proven issue with cheating? Resist the temptation to panic-hurl mud at the wall, first. 

Yes, if you’ve locked-down your processes with every preventative technology under the sun, you might have curtailed cheating. But the baby-with-the-bathwater approach has hidden costs (see question #4 👀). 

The best approach to mitigating cheating is tailored to your specific needs, on a by-process, by-role, and by-region basis. 

(Flexibility aside though, it’s critical to be fair. We’d never recommend you add measures for some candidates but not others within an open role. Diversity and inclusion is crucial to the candidate experience – and your organization’s overarching business goals.)

Choose technology that offers plenty of functionality to crack down on cheating – with the flexibility only to use it where it’s needed. With CoderPad, that anti-cheating toolkit could include:

• Code playback to see how candidates wrote their code
• IDE exit detection to know if/when candidates left their test
• Copy/paste tracking to spot if code might’ve originated elsewhere
• Plagiarism detection to spot if exact code is reused
• AI follow-up questions to check candidates’ understanding of code
• Candidate flagging to identity possible suspicious behavior
• Location tracking to check candidates are based where they say
• Anomaly alerts for unusual candidate activity or performance 
• Test performance tracking to detect unusual improvement on retakes
• Webcam proctoring and AI analysis to flag suspicious behavior 

🔖 Related read: 18 features your tech assessment tool should have to prevent cheating

The point is, let the problem lead the solution. If you use tactics like proctoring recklessly, you risk damaging the candidate experience. And ultimately, hurting your ability to hire.   

❓ What are your wider recruitment challenges?  1. Not enough quality applications 2. High candidate drop-off rate 3. Low offer acceptance rate  4. _______________________

How you choose to tackle cheating has a knock-on impact elsewhere in your hiring process. 

If your offer acceptance rate is low, for example, that points to a candidate experience problem. You could take a hardline approach to preventing cheating, with sweeping prohibitions against copy-paste, GenAI, and so on. But is cheating prevalent enough to justify an approach that damages your CX so much? 

Likewise, if you’re struggling to attract enough quality applications. Is cheating a bigger problem than employer branding? Can you afford a process that treats engineers like naughty children? How will that impact your reputation?

On the other hand, say your managers are frustrated by wasting time interviewing poor-quality candidates. With nearly half of jobseekers now admitting to using ChatGPT to write cover letters or resumes, it’s harder than ever for recruiters to screen successfully. 

A dedicated technical screening platform can remove guesswork from the process, sending fast, flexible, and fair technical assessments – so you’re moving the right candidates forward. 

When you’re developing your cheating prevention strategy, think bigger-picture than “stopping cheating”. What are the potential hidden costs? Which compromises are you willing to make? There’s no right or wrong – but make a conscious choice.  

Cheating mitigation template 📋

Working through these four questions should give you more clarity on what the right approach to cheating mitigation looks like for your organization. Here’s a template to help you frame your strategy.

	Prevent cheating	Detect cheating	Respond to suspicious activity
For every role	·  Real-life scenario based questions ·  Value-add process that helps candidates make an informed decision ·  Custom-code language-agnostic questions ·  Gamified coding exercises ·  ____________ ·  ____________	·  Test performance tracking ·  Candidate flagging ·  Code playback ·  ____________ ·  ____________	·  Follow-up questions to understand coding rationale (AI-generated or live) ·  ____________ ·  ____________
For high-risk areas: 1. _____ 2. _____ 3. _____	·  Candidate warning message ·  Question randomization ·  Copy-paste detection ·  Question timers ·  ____________ ·  ____________	·  Code similarity check ·  Webcam proctoring ·  Anomaly alerts ·  IDE exit detection ·  Location tracking ·  ____________ ·  ____________	·  Update questions to respond to content leaks ·  Reject candidates with suspicious activity ·  ____________ ·  ____________

A template like this is the foundation of an agile, flexible approach to cheating mitigation that better meets everyone’s needs.

Put the battering ram down…

Cheating is a high-stakes issue. But that doesn’t mean you need a battering ram. 

Treating all candidates like the lowest common denominator is a recipe for disaster. Cheating is nuanced and sensitive: you need to tread lightly. 

More isn’t better. Proportionate is better. Justified is better. Calibrated is better.

There’s no silver bullet, switch-it-on-and-run solution here. Instead, there’s an opportunity to step back and really evaluate the strategy behind your hiring. To integrate concerns about cheating with your overall ambitions for recruitment and the business. 

That’s not the quick fix you might want. But it’s the sustainable, long-term, measured approach that’ll most benefit the organization. 

Learn more about how CoderPad can help mitigate cheating in your tech hiring process.