QuantumLearning Machines

◆ PRODUCTS

QLM StudentSTEM simulations, Socratic tutoring, and learning evidence for K-12 learners.QLM WorldsStudent and parent-facing interactive Worlds learners can try by grade.QLM LabPathAI-guided curriculum paths where every important concept becomes a World lab.QLM Evidence ResidencyCareer-connected work simulations for new grads and career centers.QLM EnterpriseStudio, TeacherOS, dashboards, governance, and evidence infrastructure.

◆ POPULAR USE CASES

AI-proof assignmentsConvert traditional assignments into process-based simulations.STEM simulations for schoolsHands-on science and engineering with evidence.Hiring evidenceRun cohorts through realistic work simulations before final interviews.

◆ BY ROLE

For Students Practice STEM, get Socratic help, and build evidence of learning.For Teachers Save time, assign simulations, see misconceptions, and intervene faster.For Schools & Districts Deploy simulation-based STEM learning, TeacherOS, and evidence dashboards.For Higher Education Convert AI-vulnerable assignments into process-based assessments.

◆ WORKFORCE

For Career Centers Help students and alumni build job-ready evidence through residencies.For Employers Reduce screening hours with cohort-based work simulations and readiness packets.For L&D / CLOs Move training from completion to demonstrated capability.

Research Overview Our approach to learning science, assessment, and AI.Open-Source Tutor Model cards, benchmarks, and transparency.Assessment in the AI Era Why final-output assessment is breaking.Simulation-Based Learning How prediction, action, explanation, and transfer create evidence.Responsible AI Privacy, guardrails, Socratic tutoring, and human-in-the-loop design.

Loading world...

Building the open measurement layer for education AI.

NeuralPath

band 6-8 · mission — · seed —walk \u00b7 table \u00b7 pool

Missions

Invariant witness

phenomenon—

printed—

A model is arithmetic that error pushes around: every update is a printed nudge, memory of the seen is not mastery of the unseen, and the model's tilt is the training pool's counted composition wearing a machine's face.

0 events · chain 00000000

A nudge, a table, a pool

A model trains by arithmetic: error pushes the knob, memorization is not learning, and the pool's composition tilts the model's per-class accuracies. Commit a prediction before every run.

Choose a mission to open its prediction gate.

PredictTestExplainReviseDefendTransfer0/6 evidence steps