Reference

Hiring assessment glossary

The concepts behind AI-generated, quality-gated hiring assessments — defined plainly, each linked to a deeper explainer.

Adverse impact: A hiring practice that disproportionately screens out a protected group, measured by the four-fifths rule. It can occur even without intent, which is why assessments should be audited for it.
AI fluency: A measure of whether a candidate works effectively and responsibly with AI tools — knowing when to trust output, when to verify, and how to use models with judgement.
AI Sandbox: A live exercise where a candidate uses AI tools to fix or build a working artifact, measuring prompt craft, verification instinct and judgement — the applied version of AI fluency.
BARS (behaviourally-anchored rating scale): A scoring rubric that defines each level by specific, observable behaviours rather than vague adjectives, so two reviewers rate the same answer the same way.
Behavioural pillar: The hiring pillar that scores work style and traits against behaviourally-anchored rubrics instead of gut feel, turning soft signals into comparable ones.
Cognitive pillar: The hiring pillar measuring reasoning, pattern-finding and learning speed — a strong predictor of potential, but weak as a standalone gate for job skill.
Concept tree: A structured map of the sub-skills a competency contains. Questions are generated per node so an assessment covers a skill systematically instead of sampling it at random.
Domain pillar: The hiring pillar that tests whether a candidate can actually do the work — the role's real skills — using per-role questions grounded in concept trees.
Five pillars: The five distinct, separately-scored signals of a hire: cognitive ability, situational judgement, behavioural traits, domain skill and AI fluency, weighted per role.
Per-job generation: Building an assessment on demand from a job description rather than selecting it from a shared library — so there is no static, shared answer key to leak.
Quality gate: A two-phase check — structural rules plus an independent AI judge — that every generated question passes before a candidate can see it, with failures quarantined.
Quality of hire: A feedback loop that validates assessment predictions against real post-hire outcomes, so the assessment learns whether it actually predicted performance.
Situational judgement: The hiring pillar that presents realistic, ambiguous work scenarios and scores how a candidate prioritises, escalates and decides — judgement rather than recall.
Three-tier bank: A question-bank architecture that keeps content fresh per job and deduplicates by content fingerprint, so answers cannot be memorised or leaked.

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