We assess every course across eight independent planes — from pedagogical design to data protection. The planes run separately and combine into a single traffic light. Every observation is a structured “finding” with a clear source and confidence level.
Deterministic findings have a confidence of 1.0. AI findings go through two-phase verification — in the second round the model checks them against the data. The confidence threshold rises with depth.
Confidence thresholds by depth
An LLM finding receives a 0–1 confidence from two-phase verification. How many findings make it into the report and what gets flagged as “needs review” is governed by the depth:
Depth
Hide below
“Needs review”
Confident finding
Overview
all except > 0.75
—
> 0.75
Standard
< 0.4
0.4 – 0.7
> 0.7
Detailed
< 0.3
0.3 – 0.7
> 0.7
Three depths — cumulative
Overview · free
Free Traffic Light
A per-plane traffic light + the 3–5 most critical findings. Shows whether the course is on fire.
These areas are outside the automated audit — the report marks them as “handled individually”:
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In-depth content audit of multimedia — branched H5P scenarios and long videos are not assessed for content.
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Full WCAG accessibility audit — the tool performs only basic checks within planes 4 and 5.
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Content quality of SCORM/H5P packages — plane 3 evaluates only the technical deployment and tracking.
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Applying fixes in Moodle — the audit is an analysis and report, not repairs; those are a separate paid add-on.
The price is never computed by AI — it is set by a separate deterministic calculator. The report is indicative, not a legally binding assessment.
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Reports never name specific AI models — each finding carries only its source (deterministic / AI / vision / hybrid) and a confidence level. All 8 planes are always evaluated (depth controls detail, not plane selection) — at the Free level only deterministic checks run; planes with in-depth AI analysis are fully evaluated in Standard and Detailed, and plane 7 only with a connected Moodle API.