Adaptive assessment infrastructure for the AI era: simulations, Socratic tutoring, and living skills profiles that capture how people think — not just what they produce.
Generative AI can now produce or heavily assist many essays, answers, and credential artifacts. Traditional final-output assessment is under pressure: it often captures completion, recall, or polished output while under-capturing the reasoning process.
We changed the measurement. When assessment is the performance — a simulation where decisions, explanations, revisions, and evidence are captured — AI has less room to substitute for observed human reasoning.
Q is designed to avoid answer-first help. It uses task context and misconception signals to ask targeted questions so students keep doing the thinking.
1,180 learning missions across 20 pathways. Every concept follows a 5-mission sequence: discover, challenge misconceptions, practice with reasoning, explain, and transfer to new contexts. Free AI tutor that asks questions — you build the understanding.
110+ diagnostics. Branching-decision Ops and residency-style simulations. Score ranges and evidence artifacts over time.
AI workforce mastery and residency pathways. Scenario-based measurement across role tracks.
QLM is not a collection of activities. It is a structured learning evidence system where every foundational concept has a progression, misconception traps, practice with reasoning, explanation demands, and transfer challenges.
The student predicts 2/7. The fraction model reveals the pieces are different sizes. The system detects the misconception, scaffolds with "Can you combine pieces of different sizes?", and guides the student to discover common units. The teacher sees which students added denominators and receives a 5-minute intervention prompt.
Actively seeking K-8 school and district partners for pilot validation.
Pilot Information →QLM detects 103 common misconceptions in real time, groups students who share the same error, and generates intervention cards with specific teacher prompts, recommended manipulatives, and follow-up missions. Three intervention levels: 5-minute micro-interventions, 15-minute small-group reteach, and full pathway reassignment.
Not "wrong, try again." Not answer-first tutoring. Q uses task context and misconception signals to ask targeted questions that help students discover the next step themselves.
Model weights on HuggingFace under Apache 2.0. School districts deserve to verify how an AI tutor works — not trust vendor marketing.
Training-transfer research has long shown that completion does not guarantee behavior change. QLM measures cognitive capability before, during, and after training.
AI-vulnerable essays, quizzes, and certification tasks can be redesigned into simulations and process-evidence tasks that make reasoning visible.
Submit an Assignment →Grounded in established assessment, tutoring, and process-evidence research; QLM validation is reported separately as pilots mature.
Every outcome uses measured cognitive-profile evidence, not self-report alone.
View All Outcomes →Free student and teacher entry points. Open-source model work. Paid organizational tiers are available.