A customizable AI accelerator for K-12 EdTech platforms, curriculum publishers and assessment providers. Go beyond scores and dashboards. Embed explainable mastery insights, misconception detection and teacher-guided next steps into your existing product, without building the intelligence layer from scratch.The engine advises. The teacher decides.
Your platform already collects quizzes, exit tickets, practice sets and short written answers. AXIOM turns that data into insight teachers can inspect and act on, with the final decision left to them.
Every metric shows its Why.
Wrong answers mapped to the ideas behind them.
Ranked next steps with the calculation visible.
Teachers review grading, approve, adjust, or dismiss any recommendation
Classroom problem | What it means for your platform | What AXIOM adds |
|---|---|---|
Scores don’t explain misconceptions | Reporting shows results but doesn’t help interpret why students got them wrong | Maps each wrong option to the misconception it represents |
Differentiation takes manual effort | The product doesn’t give enough personalized support | Groups students by shared misconception and places at-risk students on a watch-list |
Teachers struggle to act on results | A gap remains between assessment and the instructional workflow | Ranks next steps: reteach, address a misconception, spaced review, check in, extend |
Written responses wait days | Written work sits outside the fast feedback loop | Pre-scores written answers for the teacher to review |
AI recommendations are hard to trust | Automation without transparent logic or teacher control slows adoption | Lets teachers approve, adjust, override or dismiss, and learns from those choices |
Mastery for each student and concept is estimated with a recency-weighted Bayesian method. Four class KPIs (class mastery, students at risk, practice completed, concept coverage) each show their Why.
Each wrong option maps to the misconception it represents. A student counts as holding a misconception after choosing it on two or more different questions. Students fall into suggested reteach, core and extend groups, which the teacher can override.
Low mastery, falling accuracy and low practice place students on a watch-list at three severities (Escalate, Act, Watch), each with the reasons and a suggested action. Teachers acknowledge, snooze or resolve. A resolved student returns if the picture gets worse.
Next steps (reteach, address a misconception, spaced review, check in, extend) are ranked by expected gain × students reached ÷ minutes needed. Each comes with its evidence and a visible priority calculation.
The teacher approves, adjusts (action, minutes, students) or dismisses each recommendation. Every decision goes into an audit trail and re-ranks future suggestions.
A lesson brief covers readiness with its Why, key ideas, a suggested demonstration, expected misconceptions with evidence counts, hinge questions and a plan for each group. Each group gets a practice set aimed at the misconceptions it holds, plus review items on any concept.
Written answers get an AI pre-score on accuracy, clarity, key terms and reasoning, with highlighted phrases and feedback. The teacher’s score is final and calibrates the next pre-scores.
Every metric shows its Why. Core measures are deterministic, and AI only drafts and pre-scores.
Approve, adjust, override or dismiss. Every decision is recorded, and nothing reaches students until the teacher assigns it.
Adapted to your curriculum, data and platforms, not an off-the-shelf product.
AXIOM is an accelerator, not a boxed product. We start from a working foundation and shape it to your assessment data, curriculum and classroom workflow.
We review your assessment data, item structure and the systems AXIOM needs to work with.
Concepts and wrong-answer misconception mappings are set up for your content.
AXIOM connects to your existing curriculum, assessment and learning environments.
Teachers review real recommendations on your data before wider rollout.
That’s long enough to see an eLearning ecosystem as one system: integrations, compliance, procurement, accessibility, assessment logic, analytics and the stack you already run.
Our R&D team, with 6+ years of experience, tests whether an idea is feasible, finds where AI helps a learning workflow, trials the architecture and gathers the analytics that show whether it deserves further investment.
Start from a ready foundation and a working prototype, not from scratch.
AXIOM adapts to your curriculum, assessment data, existing platforms and processes.
Book a demo and see AXIOM turn answer-level data into explainable next steps, with the teacher in control.
Those tools decide for the teacher. AXIOM proposes and explains: groups, recommendations, practice sets and lesson briefs are suggestions the teacher can approve, adjust, override or dismiss. Nothing reaches students until the teacher assigns it.
Every number is calculated from the class’s answers by fixed rules and has a Why: how many answers were correct, how much the class average contributed, which students are below the threshold, and the full priority calculation of each recommendation. Any heatmap cell opens the individual answers behind it.
No. All measures, groups, risk levels and rankings are deterministic. A language model only drafts lesson-brief wording and pre-scores written answers; the teacher can edit or reset any draft and confirm every score. Without a model, briefs use templates and grading uses a rule-based grader.
Each decision adjusts a weight for that type of recommendation — approve +0.10, adjust +0.05, dismiss −0.10, kept between 0.5 and 1.5 — and the weight feeds the ranking, so the list re-orders toward what the teacher actually uses. Group overrides, edits and grades are recorded in the audit trail.
Answers are scored on four named dimensions with fixed weights, and highlights quote only phrases that are in the answer. The teacher’s score is always final. AXIOM shifts new pre-scores toward the teacher’s past corrections and shows the teacher’s recent scores to the model as examples; a calibration card reports, on corrections it did not learn from, how far the AI was from the teacher before and after adjusting — including when the adjustment is not helping.
Their own name, assigned practice, written prompts with the teacher’s score and comment, and progress per concept as a level from “Getting started” to “Mastered”. No percentages, no rankings, and never risk flags, groups, AI pre-scores or other students’ data — the student interface returns only an allow-listed set of fields.
Answer-level records: which student answered which question, the option chosen, whether it was correct, and when. Each question carries a concept, a difficulty and the misconception behind each wrong option. Practice and written answers given inside AXIOM flow in directly; existing assessment and roster sources are connected through the API as part of deployment.
The engine needs no demographic data. AI features are optional and can be switched off. When they are on, student names are removed before lesson-brief text is sent to the model and grading sends the answer text only; the API key is supplied at start-up and never built into an image. Data stays in the customer’s deployment.
Docker Compose with two containers — the Python backend and nginx serving the web interface — with data in a persistent volume, in the customer’s own cloud; the model is set in configuration. Teacher and student screens are built to WCAG 2.1 AA, keyboard operable, and checked automatically on every route.
