AINA data engine room - Codex - 2026-06-13

Authored Lesson Depth Gate v1

Representative role-family curriculum modules were audited for authored lesson depth: resolved lesson nodes, role fit, level fit, teaching signal density, pedagogy signal, workflow/AI-affordance grounding, practice/rubric links, source-path quality, and preserved GDPval structured model/domain calibration boundaries.

The Single Idea

This gate separates authored lesson depth from simple content coverage, while keeping the GDPval calibration decision in model-review state.

01

Evidence Snapshot

passstatus
48modules
48depth-ready
48role fit
48level fit
48signal density
48pedagogy signal
TrueGDPval held
02

Role Family Lesson Depth

FunctionTitleStatusModulesDepth ReadyRole FitLevel FitGaps
administrationassistant store managerpass4444none
customer_successaccount managerpass4444none
data_analyticsdata analystpass4444none
design_creativegraphic designerpass4444none
financesenior accountantpass4444none
legal_complianceparalegalpass4444none
marketingmarketing managerpass4444none
operationsproject managerpass4444none
people_hrhuman resources generalistpass4444none
productproduct managerpass4444none
salessales managerpass4444none
strategy_consultingleasing consultantpass4444none
03

Gap Classification

GapSeverityCountNext Action
none-0No authored lesson depth gaps.
04

Interpretation

The content coverage gate proves breadth; this gate proves teaching depth. It also verifies that the GDPval large-rubric calibration packet remains a structured model/domain decision, with no auto-approval and no public or external runtime.
Where to start
Use the JSONL rows to repair the first module with role-fit, level-fit, or source-path quality gaps, then rerun this gate and validation.