ZeqAdaptiveLearning

Adaptive learning, assessment, curriculum.

• Protocol ID — zeq-adaptive-learn
• Category — Education
• Endpoint — POST /api/edu/adaptive
• Auth — api-key
• Rate limit — 30/min
• Version — 1.287.0
• Precision — ≤0.1% (KO42-enforced)

What it does

Adaptive learning engine using R(t)-modulated knowledge state estimation. Item response theory (IRT) with Zeqond-paced mastery gates, spaced repetition scheduling.

Signature

Request

POST /api/edu/adaptive

Param	Type	Required	Default	Description
studentId	string	✓	—	Learner identifier.
responses	array	✓	—	[{ itemId, correct, responseTime_ms }]
domain	string		—	Subject area.

Response

{ abilityEstimate, nextItems, masteryLevel, retentionSchedule, zeqond }

Runnable example

curl -sS -X POST \
  -H "Authorization: Bearer $ZEQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "studentId": "<value>",
  "responses": [],
  "domain": "<value>"
}' \
  "https://api.zeq.dev/api/edu/adaptive"

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/adaptive in a language SDK so builders can call it in three lines.
• Medium — publish a reference integration demonstrating ZeqAdaptiveLearning alongside a real workload, with pulse-aligned metrics.
• Far — propose ZeqAdaptiveLearning 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.