QuantumLearning Machines
Quantum Learning Machines

Open Data API

Three open educational datasets for researchers, developers, and AI tutor builders. All data licensed under CC-BY-4.0.

423 misconceptions1,884 learning graph edges118 ASSISTments linksCC-BY-4.0
Endpoint
GET https://play.quantumlearningmachines.com/api/labpath/dataset-export
Parameters
NameDefaultValues
datasetallmisconceptions | learning-graph | standards | interop | all
domainallphysics | math | biology | chemistry | cs (misconceptions only)
formatjsonjson | jsonl (misconceptions only)

Datasets

Misconceptions

423 entries

K-12 STEM misconception ontology. 423 constructs across 9 domains (physics, biology, chemistry, math, cs, earth science, ELA, health, history). Each entry includes the student belief, scientific reality, counterexample, diagnostic question, trigger patterns, severity, persistence, research source, and teacher intervention.

All entries
curl -s "https://play.quantumlearningmachines.com/api/labpath/dataset-export?dataset=misconceptions" | python3 -m json.tool
Filter by domain
curl -s "https://play.quantumlearningmachines.com/api/labpath/dataset-export?dataset=misconceptions&domain=math"
Download as JSONL
curl -s "https://play.quantumlearningmachines.com/api/labpath/dataset-export?dataset=misconceptions&format=jsonl" -o misconceptions.jsonl

Learning Graph

1,884 entries

Skill adjacency graph connecting simulation modes. 1,884 directed edges encoding prerequisite relationships, shared concepts, and misconception overlap between modes. Each edge includes priority level (high/medium/low), concepts, misconception references, and student- and teacher-facing explanations.

Example
curl -s "https://play.quantumlearningmachines.com/api/labpath/dataset-export?dataset=learning-graph" | python3 -m json.tool

Standards Alignment

NGSS standards mapped to simulation modes. Each entry links a mode to a standards code, description, grade band, disciplinary core idea, and discipline.

Example
curl -s "https://play.quantumlearningmachines.com/api/labpath/dataset-export?dataset=standards" | python3 -m json.tool

Interop: ASSISTments + Learning Commons

118 entries

Crosswalk mapping 118 math misconceptions to ASSISTments practice assignments and 31 misconceptions to Learning Commons Knowledge Graph standard codes. Each ASSISTments link is a deep URL to free remediation practice. Each Learning Commons link maps to a CCSS or NGSS standard code usable with the Knowledge Graph's find_misconceptions_for_standard() tool.

Example
curl -s "https://play.quantumlearningmachines.com/api/labpath/dataset-export?dataset=interop" | python3 -m json.tool
Citation
QLM LabPath Dataset v1.0 Quantum Learning Machines, 2026 License: CC-BY-4.0 https://play.quantumlearningmachines.com/api/labpath/dataset-export

Measurement SDK

qlm-measure — the public interface layer for the open measurement infrastructure. Evidence events, record verification, and dataset clients. Apache-2.0.

npm
npm install @qlm/measure
pip
pip install qlm-measure
Source
github.com/QuantumLearningMachines/qlm-measure
Evidence Events
Typed vocabulary for student interactions with scaffold level, depth, and epistemic mode.
Record Verifier
Verify hash-chain and posterior-chain integrity. Catches tampering without any estimation math.
Dataset Clients
Query the misconception ontology, learning graph, and standards alignment. Zero-auth.
Measurement Emitter
Buffered telemetry with sendBeacon. Survives page unload.

Building the open measurement layer for education AI.

All data licensed under CC-BY-4.0. SDK licensed under Apache-2.0.

For research collaboration: hello@quantumlearningmachines.com