AI is no longer a novelty in hiring. Candidates are openly admitting to using ChatGPT to draft resumes, prep interview answers, and even simulate mock interviews. On the other side, recruiters are testing AI recruiting tools to screen resumes, generate job descriptions, and analyze interview transcripts.
The result? A strange arms race where AI-written applications meet AI-enabled hiring processes. And somewhere in the middle, recruiters still need to identify the genuine humans who can actually do the job.
One of the most effective defenses against AI-generated fakery lies in a simple tool we’ve had all along: situational interview questions.
Recruiters have already sensed this in practice. AI-generated interview answers may sound polished when questions are predictable, but situational and judgment-based prompts tend to expose their weaknesses. Responses often come across as generic, formulaic, and lacking depth.
So why are situational questions so much harder for AI to fake? And how can recruiters design them to separate authentic candidates from AI-polished imposters?
Let’s break it down.
Predictable questions vs unpredictable scenarios
Most interview prep guides, even before ChatGPT, trained candidates to expect certain questions:
- What are your greatest strengths?
- Tell me about a time you worked in a team.
- Where do you see yourself in five years?
These are predictable prompts. They map neatly to training data and standard coaching frameworks. ChatGPT excels here. It has absorbed countless examples and can spin up a confident, structured answer in seconds.
Situational questions work differently. A recruiter might ask:
“Imagine you’re covering the front desk at 5 p.m. when three customers arrive at once. One is angry about a billing error, another is in a rush to pick up an order, and the third just wants directions. How do you handle it?”
That level of specificity forces a candidate to juggle priorities, show judgment, and apply practical reasoning.
When ChatGPT tackles this, it tends to fall back on stock phrases: “I would remain calm, communicate clearly, and ensure each customer feels heard.”
It sounds neat, but it lacks the messy human detail that makes an answer believable. Real candidates reference lived context: “At my last retail job, this exact thing happened during the holiday rush. I asked the rushed customer to give me two minutes, calmed the angry customer by apologizing, and waved over a colleague to give directions.”
One is grounded. The other is too textbook.
The need for context-rich detail
Strong situational answers aren’t abstract. They’re built on context. Candidates need to consider:
- Who the stakeholders are
- What the constraints are (time, budget, authority)
- What the real-world stakes feel like
For example, in a finance internship interview, a strong human answer might reference clients upset about two consecutive quarters of underperformance, and describe how the intern managed expectations while escalating to a senior advisor.
ChatGPT, by contrast, tends to float at the surface: “I would reassure the client, explain the situation clearly, and collaborate with my team to resolve the issue.”
Polite. Well-structured. But ultimately hollow.
Follow-up questions expose weak answers
Recruiters rarely stop at a single prompt. They probe.
- “What exactly did you say in that moment?”
- “Why did you choose that option instead of another?”
- “What was the outcome?”
Here’s where AI answers collapse. Because ChatGPT isn’t recalling lived experience, it can’t sustain follow-up detail. Its responses start to contradict themselves or recycle the same surface-level reasoning.
Humans, by contrast, are comfortable grounding responses in personal memory. They can give second and third layers of detail that feel coherent and authentic.
Situational questions reveal reasoning, not polish
Another reason situational questions are harder for AI: they don’t reward memorization. They test real-time problem solving.
Recruiters can see how a candidate:
- Organizes thoughts under pressure
- Balances competing priorities
- Weighs trade-offs between short-term fixes and long-term consequences
- Communicates reasoning in a natural way
This is exactly why many hiring teams using Truffle recommend weaving situational prompts into one-way video interviews. As one recruiter told us: “If you ask someone about conflict, and push them for an example, it’s a lot harder for ChatGPT to magic that up convincingly.”
How to design situational questions AI can’t fake
Not all situational questions are created equal. Some are still broad enough for AI to bluff. The key is specificity and constraint.
Here are a few design principles:
- Ground the scenario in real context
Instead of “Tell me about a time you handled multiple priorities,” try: “You’re leading a shift when two employees call out sick, the register goes down, and a delivery arrives early. Walk me through your first three steps.” - Introduce constraints
Add time limits, limited resources, or authority boundaries. Candidates have to show creativity under pressure, which AI often misses. - Ask for step-by-step reasoning
Don’t just ask “What would you do?” Ask “Why would you prioritize that step first?” This exposes real thinking. - Probe for outcomes and learning
Follow up with: “What was the result?” and “What would you do differently next time?” Genuine candidates have reflections; AI tends to recycle clichés about teamwork and growth. - Blend with behavioral questions
Situational and behavioral questions work best together. Situational probes test forward-looking reasoning, while behavioral ones check for patterns in past behavior. Together, they make it very hard for AI-scripted prep to pass undetected.
What this means for recruiters
The temptation for candidates to lean on ChatGPT isn’t going away. In fact, some AI-generated answers are so polished they outscore real candidates on dimensions like clarity and grammar.
That means recruiters can’t simply rely on gut instinct to “spot the AI.” Instead, they need structured defenses built into their process:
- Use situational prompts that demand practical reasoning
- Build in follow-up layers to test depth
- Combine structured scoring with human judgment
And when possible, pair situational questions with technologies that help recruiters review more efficiently. For example, Truffle’s one-way video interviews let candidates record situational answers asynchronously, while recruiters get transcripts, summaries, and evaluation cues. That makes it easier to sift through dozens of candidates without sacrificing depth.
Looking ahead
As generative AI gets better, some of today’s weak spots may close. ChatGPT might learn to produce more detailed situational answers, perhaps even simulate plausible context. But for now, situational judgment remains a frontier where human reasoning shines through.
Recruiters who lean on situational prompts aren’t just catching AI cheats. They’re also doing better hiring. Because at its core, the ability to weigh messy trade-offs and adapt under pressure is exactly what predicts success in the workplace.
In a hiring landscape flooded with AI-polished resumes and pre-scripted answers, situational questions remind us of something fundamental: work is unpredictable. The best hires aren’t those with the smoothest scripts, but those who can think, decide, and act when the script runs out.
Key takeaway: Situational questions are harder for ChatGPT to fake because they demand context, detail, and reasoning under constraint. Recruiters who use them—especially in structured, video-based formats—gain a powerful advantage in cutting through the noise of AI-generated applications.
