The research
How we measure capability.
Eight metacognitive dimensions drawn from cognitive science, philosophy of knowledge, and the emerging literature on human–AI collaboration. Each has a formal construct definition, intellectual provenance, and an empirical reference list.
Methodology
Construct-Templated Adaptive Interviewing.
These are the qualities that distinguish high performers in complex, AI-era engineering work — and the ones that conventional technical-interview instruments cannot detect. They cannot be retrieved from a knowledge base. They are forged through real experience and legible only to evaluators who know what to look for.
We call the methodology Construct-Templated Adaptive Interviewing, or CTAI. Every candidate is asked different questions, drawn from their own CV — but the underlying constructs and scoring rubrics are identical. A self-taught engineer is evaluated against the same evidence bar as a Cambridge graduate.
No dimension may receive a high score without a specific verbatim statement from the candidate cited as evidence — drawn either from the Round 1 transcript or the Round 2 reflection conversation, or from observable patterns in the Round 2 trace. Scores are grounded in what was actually said and done, not overall impression.
The eight dimensions
Capability is a shape, not a number.
- 01
Judgment Under Ambiguity
Committing to a defensible course of action when information is incomplete — without either paralysis or false confidence.
Provenance: Knight, Risk, Uncertainty and Profit; Tetlock's superforecasting work.
- 02
Tacit-Knowledge Articulation
Surfacing knowledge that lives in practice rather than in text — the things experienced engineers know but cannot easily say.
Provenance: Polanyi, The Tacit Dimension; Nonaka & Takeuchi; Collins.
- 03
Intuition Under Data Scarcity
Recognition-primed judgment that distinguishes real expertise from vocabulary. The pattern-matching that fires before you can explain it.
Provenance: Klein, Sources of Power; Dreyfus & Dreyfus; Kahneman & Klein.
- 04
Psychological Safety & Collective Learning
Creating conditions where errors surface early and dissent is voiced — the team-level capability that lets engineering organisations actually learn.
Provenance: Edmondson, The Fearless Organization; Google's Project Aristotle.
- 05
Creative Problem Reframing
Recognising when the team is solving the wrong problem — and reformulating it before more effort is poured into the wrong shape.
Provenance: Schön, The Reflective Practitioner; Dorst, Frame Innovation.
- 06
Ethical Reasoning in Practice
Feeling the weight of real tradeoffs and navigating them with integrity — the practical wisdom that abstract ethics training does not produce.
Provenance: Aristotle's phronesis; Rest's four-component model; the applied AI-ethics literature.
- 07
Transformative Learning From Experience
Updating prior beliefs in proportion to disconfirming evidence. The capacity to be changed by experience, not merely accumulate it.
Provenance: Flavell; Kolb; Mezirow; Argyris & Schön.
- 08
Human–AI Collaboration Intelligence
Fluent, calibrated orchestration of AI tooling — the dimension no other interview measures. Where to delegate, where to verify, where to override.
Provenance: Mollick, Co-Intelligence; Dell'Acqua et al., Navigating the Jagged Technological Frontier.
More questions?
The FAQ has answers on rounds, scoring, calibration, integrations, and what we deliberately don’t measure.