Canonical Artifact Name: Education OS Kernel v1.0 — Human Outcome Physics Kernel
Artifact Class: Executable Human Systems Diagnostic Kernel
Artifact Version: v1.0
Artifact Authority: eduKate Singapore
Canonical ID: EducationOS-Kernel-v1.0
Block 1 — System Boundary
System: An organization that adopts AI tools at scale over a 6–18 month window and experiences productivity gains but declining decision quality, rising errors, and weakened human capability.
Included: human skill pipeline, process quality, incentives, measurement integrity, AI usage patterns, oversight, feedback loops, and operational outcomes.
Excluded: model branding wars, moral blame narratives, and individual scapegoating not tied to system mechanics.
Block 2 — D/L/T Score Table (0–5)
| Axis | Score | Justification |
|---|---|---|
| D (Depth) | 2/5 | Human capability thins as staff outsource thinking to AI; domain fundamentals weaken. |
| L (Load) | 2/5 | Under stress, error rates spike because humans cannot catch AI failures reliably. |
| T (Transfer) | 1/5 | AI “help” does not transfer into durable human skill or stable institutional learning; mistakes repeat in new contexts. |
Block 3 — Binding Constraints (e)
- Measurement constraint: success metrics reward speed and volume more than correctness and long-term capability.
- Oversight constraint: limited expert capacity to review, audit, and correct AI outputs at scale.
Block 4 — Trajectory Physics (t)
Rise: Early AI adoption boosts speed, output volume, and perceived productivity.
Stall: Quality issues emerge; rework increases; teams rely on “patch review” and firefighting.
Regression: Decision quality declines, trust fractures, and the organization becomes dependent on brittle automation.
Lock-in risk: Humans lose depth and cannot recover competence quickly; the organization becomes permanently faster-but-wrong unless capability pipelines are rebuilt.
Block 5 — Failure Signature
Primary failure: D-FAIL (Depth failed first)
Reason: The organization replaced skill-building with tool-use; intrinsic competence eroded, making errors invisible until late.
Block 6 — Recovery Mode
Selected: Depth Repair.
Block 7 — Retest Probe
Probe A – Human competence retention
Define 5 core tasks that must be solvable without AI (baseline competence).
Pass: ≥80% of relevant staff can complete these tasks unaided with acceptable quality for 3 consecutive months.
Probe B – Error detection under load
Run weekly “red team” tests where AI outputs contain embedded mistakes.
Pass: humans detect ≥85% of critical errors within set review time for 8 consecutive weeks.
Probe C – Durable learning transfer
Track repeated-error rate across new projects.
Pass: the same class of failure does not recur across two consecutive cycles, and documented corrections are applied team-wide.
Re-run rule:
If probes pass for 8 consecutive weeks, raise D by +1 and re-execute the kernel.
Suggested WordPress Details for Run-009
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Canonical ID: EducationOS-run-009-v1.0