Description:
Hiring managers increasingly worry about applicants submitting AI-assisted designs, writing samples, or code. What practical checks, interview tasks, and technical tools can be used to verify originality and evaluate a candidate’s true skills? Which red flags are reliable versus misleading, and how can I balance skepticism with fair assessment during hiring?
5 Answers
Ask candidates to explain decisions behind their work, like why they chose a certain design or approach. AI can generate output but struggles with reasoning about trade-offs or context. During interviews, give unexpected tweaks to original tasks and see how they adapt on the spot. Also check if their style is consistent across samples and live work. Red flags are when someone can't discuss or change their own submitted work naturally. Balance skepticism by focusing on problem-solving skills, not just polished results.
- Anonymous: Great approachβcontextual questioning reveals depth AI lacks. In one hiring case, probing design rationale uncovered inconsistencies in 3 out of 10 portfolios, leading to selecting candidates with genuine insight. Follow-up: How do you handle candidates who prepare scripted explanations that mimic human reasoning?Report
- A. E.: Thanks for sharing that exampleβspot on. When candidates rely on scripted answers, I try to pivot with unexpected, specific follow-ups that require on-the-spot thinking. This helps reveal genuine understanding versus rehearsed responses.Report
require process artifacts: drafts, commits, issue discussions and a short recorded walkthrough recreating one change, pay for micro-projects, be explicit about ai rules...
- Joseph Garcia: Thanks for the detailed tips! When you say "be explicit about AI rules," do you mean setting clear guidelines on whatβs acceptable in the work?
- Addison Sullivan: Exactly Joseph! Setting clear guidelines upfront on whatβs acceptable regarding AI tools helps everyone understand expectations and keeps the evaluation fair. It also encourages applicants to be transparent about how they use AI in their work
Require small constrained takehome tied to your stack then do teach-back plus live debugging to probe depth use similarity detectors and metadata checks
Think relying solely on AI-detection tools is enough? Itβs not. The best way to spot AI-generated work is through live, interactive assessments that force candidates to explain their process and adapt on the fly. For example, after a take-home coding test, have them debug or extend their code in a live session. This exposes gaps in understanding that AI canβt fake. Red flags include inconsistent style between portfolio pieces and live work, but donβt mistake nervousness or new tools for dishonestyβalways balance skepticism with fair assessment by combining multiple evaluation methods.
I once caught a candidate whose portfolio was spotless but stumbled badly in a live Figma redesign task. Use tools like GitHubβs code history and Turnitin for writing checks. In interviews, assign quick, unplanned edits in Sketch or VS Code. If they freeze or canβt explain choices, itβs a red flag. Donβt rely on AI detectors aloneβfocus on live problem-solving and process transparency.
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