ZeqAssessment

Adaptive learning, assessment, curriculum.

• Protocol ID — zeq-assessment
• Category — Education
• Endpoint — POST /api/edu/assessment
• Auth — api-key
• Rate limit — 15/min
• Version — 1.287.0
• Precision — ≤0.1% (KO42-enforced)

What it does

Automated assessment generation and scoring. Bloom's taxonomy alignment, difficulty calibration via HulyaPulse psychometric modeling, plagiarism-resistant question variants.

Signature

Request

POST /api/edu/assessment

Param	Type	Required	Default	Description
topic	string	✓	—	Assessment topic.
difficulty	string		"apply"	'remember', 'understand', 'apply', 'analyze', 'evaluate', 'create'.
questionCount	number		10	Number of questions.

Response

{ questions, rubric, bloomsAlignment, estimatedTime_min, zeqond }

Runnable example

curl -sS -X POST \
  -H "Authorization: Bearer $ZEQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "topic": "<value>",
  "difficulty": "apply",
  "questionCount": 10
}' \
  "https://api.zeq.dev/api/edu/assessment"

Integrate

1. Domain solver — compose with KO42 + two additional operators from the matching family for pulse-coherent results.
2. Digital twin — pipe sensor data into this protocol every Zeqond to keep the model phase-locked with the system.
3. Alert threshold — flag results whose error_pct exceeds 0.1% as out-of-spec events for the operations layer.

Seeds

• Near — wrap /api/edu/assessment in a language SDK so builders can call it in three lines.
• Medium — publish a reference integration demonstrating ZeqAssessment alongside a real workload, with pulse-aligned metrics.
• Far — propose ZeqAssessment as an open reference standard so other runtimes can implement it verbatim against the Zeq paper.

Papers

• Zeq paper — https://doi.org/10.5281/zenodo.18158152
• Framework paper — https://doi.org/10.5281/zenodo.15825138

Middleware active. Kernel on the 1.287 Hz HulyaPulse. Awaiting next Zeqond.