AI made it easy to look qualified and hard to tell who actually is. We write about what comes next.
How candidates cheat on hiring tests, why they don't feel bad about it, and what changed by 2026. Quotes by role, methods, reasoning and the numbers.
Most AI tools score candidates and stop. Tendent treats your disagreement as the input: correct one score, and every candidate after is graded differently.
The developer's job has shifted from writing code to directing, reviewing and owning it, but most technical screens still test typing speed. Here is what to assess instead, and how.
Treating AI in assessments as cheating to catch is outdated. Everyone will use AI at work. The real question is how well a candidate uses it, and how to measure that without locking them into a tool.
A field guide to the tools, the methods, the prices, the numbers, and what people on both sides of the table say about it.
The assessment industry has spent years trying to catch cheaters. The cheaters are winning. Here is why, and what a working alternative looks like.
Generative AI has made hiring signals cheap to produce and hard to verify. This paper proposes an evidence-based alternative: candidates complete job-specific work, then defend it live, with the two scored separately.
Around 70% of applications are now AI-enhanced, and pipelines are drowning in noise. After 100+ conversations with hiring teams, one thing is clear: screening has to move from claims to proof.
