For most of the last two decades, the résumé did much of the heavy lifting in technology hiring. A list of programming languages, a few recognizable employers and the right certifications were usually enough to earn a first conversation.
The rise of AI-generated résumés in hiring has made that shortcut far less reliable. A Gartner survey of nearly 3,300 job candidates found that 39% used AI during the application process, and Gartner predicts that by 2028, one in four candidate profiles worldwide will be fake. Job seekers are also hearing that AI can help them write a stronger résumé, and many are taking that advice. When a résumé can be tailored to every job description in seconds, a polished document tells a hiring manager very little about how someone will perform.
Knowing how to evaluate technical talent now means looking past the paper and toward evidence of how a candidate thinks, learns and works.
Why Résumés Struggle to Predict Performance in Technology Roles
Résumés were never a strong predictor of performance. Research led by industrial-organizational psychologist Paul Sackett reexamined decades of hiring data and found that structured interviews were the highest predictor of any common selection method. Work sample tests and job knowledge tests also ranked near the top. Years of experience, one of the first things most reviewers scan for, ranked well below them.
The gap can be even wider in technology roles, where tools and frameworks change quickly. A candidate with five years’ experience on a platform that AI-assisted tooling will transform or replace may be less prepared than someone with two years’ experience who has spent their evenings learning how to use AI tools. For teams deciding how to assess technical candidates, structured interviews for technology roles are one of the most reliable tools available.
Curiosity: The Trait That Predicts AI Readiness
With AI touching nearly every technology position, hiring managers need to know more than whether a candidate can do the job as it exists today. They need to know whether that person will keep up as the job changes.
“Now that AI is impacting almost every role, managers are seeking candidates who are willing to learn and try new things,” said Andrew Jackson, President and Co-Founder of BravoTECH. “Curiosity is the trait they’re looking for. People who have tried new things are the most likely to use AI tools to increase productivity.”
The broader data points in the same direction. The World Economic Forum’s Future of Jobs Report 2025 lists curiosity and lifelong learning among the skills employers expect to grow in importance through 2030, alongside creative thinking, technological literacy and the ability to work with AI and big data.
Curiosity rarely appears on a résumé. But it shows up in conversation, which is why the shift toward behavioral interviews in tech hiring matters more than ever.
Interview Questions to Assess Curiosity
For a 30-minute technical interview, these five questions assess curiosity, each exploring a different dimension of the trait. Each one includes a follow-up that pushes past a rehearsed answer.
- Self-directed learning: “What is something you recently learned that nobody asked you to learn?” Follow up by asking what prompted the interest and what they did with that knowledge.
- Investigative curiosity: “Tell me about a technical problem you investigated beyond what was necessary to fix it.” Ask what they discovered that they weren’t expecting.
- Intellectual humility: “When was the last time you discovered that something you believed about technology was wrong?” Ask what changed their mind.
- Exploration: “If you had an entire Friday to explore any technology, with no deliverable expected, what would you investigate?” Ask why that subject interests them.
- Questioning ability: “What would you want to know about our technology environment before recommending a solution?” Ask which question they would ask first and why.
Strong answers tend to be specific. A curious candidate can name the tool, the documentation they read, the dead end they hit and what they plan to try next. Vague answers, especially ones that sound scripted, are worth probing further.
How to Evaluate Technical Talent with a Repeatable Process
The research also points to one of the most important technology hiring best practices: consistency. Asking every candidate the same core questions and scoring answers against the same criteria is what makes an interview predictive. A practical approach for technology roles combines three elements:
- A structured interview with consistent questions and a simple scoring rubric, agreed on before interviews begin.
- A work sample or practical exercise that mirrors the actual job, completed live or discussed in detail so the candidate can explain their choices.
- Curiosity-focused questions like the ones above, scored with the same rigor as technical answers.
This layered approach also makes it harder for fabricated or heavily AI-assisted applications to slip through, since candidates have to show what they know in real time.
In short, evaluating technical talent more accurately comes down to three shifts: treating the résumé as a starting point for conversation, using structured interviews and work samples to test real ability, and scoring curiosity as part of the process.
If your organization is rethinking how it evaluates technology candidates, BravoTECH provides talent solutions built around both the technical skills a role requires and the curiosity that keeps a hire valuable as AI changes the work. Get in touch to talk about your next technology hire.
Continue Reading