05 - Pilot 01
Pilot 01 - AI-Augmented Quality Engineering
Empirical validation in progress. No quantified result is published before it has actually been measured and validated. Pilot 01 is a pilot, not proof of superiority.
Research question
Does an explicit Augmentership intervention bring measurable value beyond good professional AI integration, particularly in developing, preserving and transferring Quality Engineering capabilities?
Hypothesis
Research hypothesis: condition 03 might produce, beyond condition 02, a durable and transferable capability gain. This hypothesis is not a result.
Example target capability
« A QE engineer's capability to identify relevant test risks in a complex, unfamiliar system. »
3 comparison conditions
| 01 | Usual practice | Normal QE practice without intervention. |
| 02 | AI + good integration | AI with appropriate training, professional practices, verification and governance. |
| 03 | AI + Augmentership | Same environment, with an explicit capability target, development mechanisms, agency conditions, longitudinal measurement and adaptation rules. |
Protocol
- 01Baseline assessment.
- 02Good AI integration with professional verification.
- 03Augmentership intervention with reasoning, challenge, feedback, transfer and retention of critical skills.
- 04Delayed assessment on a new but comparable system.
- 05Plausible AI-error scenarios, escalation and intervention.
- 06Decision according to pre-defined thresholds.
Experimental discipline
Randomisation where possible; team-level allocation if contamination is likely; same assessment mechanism; documented tool versions; time and support costs measured; delayed follow-up suited to the learning cycle.
Follow the results →
Augmentership Principle
« The question is not whether AI works. The question is what changes in human capability. »