Your engineers write code with AI copilots open in a split screen. They debug with AI, draft documentation with AI, and prompt their way through problems that used to take days. AI is embedded in the actual job.
And yet many companies still run technical assessments that ban AI tools entirely — testing candidates on a workflow they’ll never use once they’re hired.
Something doesn’t add up.
The Numbers Make This Uncomfortable
66% of developers prefer practical challenges that mirror their day-to-day work over abstract algorithmic puzzles. Yet most companies still run LeetCode-style screens that have nothing to do with how engineers actually spend their days.
82% of developers now use AI tools in their development process. Yet many assessments still prohibit AI use — which means you’re testing candidates on a workflow they haven’t practiced in months, while filtering out the people most comfortable with how modern development actually works.
25% of technical assessments show signs of plagiarism. AI detection tools catch some of it. But candidates who want to cheat will find a way, and candidates who are just being efficient professionals get caught in the crossfire.
Banning AI from assessments doesn’t make them more accurate. It makes them test a different, less relevant skill set — and creates an adversarial dynamic that damages candidate experience from day one.
The Real Question: Not “Did They Use AI?” but “How?”
Think about what your best engineers actually do with AI tools:
- They use it for code completion and boilerplate, freeing mental bandwidth for architecture decisions
- They prompt strategically — knowing what to ask and how to evaluate the output
- They iterate on AI suggestions rather than accepting them wholesale
- They know when AI is wrong and can debug AI-generated code
- They combine AI assistance with genuine problem-solving
Now think about what a candidate who’s cheating does:
- Pastes the entire problem into ChatGPT
- Copies the output without understanding it
- Can’t explain their approach, debug edge cases, or extend the solution
These two behaviors look very different — if you’re measuring the right things.
AI-permitted assessments, designed with the right evaluation framework, actually give you more signal than AI-prohibited ones. You see not just what candidates produce, but how they think, how they work with tools, and whether they understand what they’ve built.
What Good AI-Integrated Assessment Looks Like
Project-based work, not puzzle-based work. Instead of “implement a binary search tree from scratch in 45 minutes,” the brief is: “here’s an existing codebase with a bug in the authentication flow — find it, fix it, and improve test coverage.” This is what engineers actually do. AI assistance is natural. The assessment is about problem-solving, not memorization.
AI usage captured and analyzed, not blocked. The best platforms capture all AI interactions during the evaluation — what the candidate asked, what AI suggested, how they responded. The pattern of AI collaboration is itself signal: did they prompt well? Did they understand the output? Did they modify the suggestion or just accept it?
Behavioral signals alongside code quality. How did the candidate approach the problem? Did they read the existing code first or jump straight to writing? Did they test incrementally? These patterns — captured automatically — paint a picture of engineering judgment that code quality metrics alone can’t capture.
Consistent rubrics across every candidate. One of the biggest problems with traditional technical interviews is inconsistency. Two interviewers with different coding philosophies evaluate the same candidate and reach opposite conclusions. Structured AI-assisted evaluation applies the same rubric to everyone — reducing bias and making comparison legitimate.
Fraud Detection in an AI-Permitted Environment
There’s a real integrity challenge that AI-permitted assessments still need to solve: not all AI usage is the same.
Using Copilot to autocomplete variable names? Totally legitimate. Generating the entire solution from a prompt and submitting it without engaging with it? That’s a different situation.
Modern assessment platforms are getting better at distinguishing these. AI-powered plagiarism detection now achieves 85–93% precision in identifying when a candidate has submitted AI-generated work without genuine engagement — analyzing not just the code itself, but typing patterns, solution progression, response latency, and how the code evolved over time. These behavioral signals are very hard to fake.
The result: candidates who use AI effectively are rewarded. Candidates who use it inappropriately are flagged. Everyone who just follows manual rules and ignores available tools is correctly disadvantaged.
A Practical Transition Plan
If your current technical screening feels stuck in 2019, three steps to modernize it:
1. Audit your assessments. Map each question to a real engineering task. If candidates would never do that task without AI on the job, it shouldn’t be in your screen without it either.
2. Add behavioral criteria. Score for: how did they approach the problem? Did they understand what they built? How did they respond when they hit a blocker? These predict performance better than raw code quality.
3. Build AI collaboration into your rubric. Define what good AI usage looks like for your engineering roles and start evaluating for it explicitly. Candidates who use AI strategically are showing you a skill you actually need.
The best engineers in 2026 aren’t the ones who refuse to use AI. They’re the ones who know exactly how to use it — and when not to. Your hiring process should be able to tell the difference.
At Casuro, our assessments are built around real-world scenarios where AI assistance is expected — because that’s how real engineering work gets done. And our evaluation framework captures the full picture of how candidates engaged: what they understood, what they borrowed, and whether they could extend it. See how it works →
