Training that keeps pace with change
Faster onboarding and retraining
Employees, partners and technical specialists adapt to new roles and products faster.
Consistent training across locations
One standard of training for distributed teams, partners and franchises.
Lower content maintenance effort
Less manual rework of courses after documentation changes.
Reduced training-related risk
Fewer cases of people following outdated or incorrect instructions.
From documentation bottlenecks to ready-to-review training
Training problem | What Learning Hub does | Outcome |
|---|---|---|
Course creation starts from a blank page | Builds course structures from your documentation, following your templates and rules | Faster first drafts, consistent structure |
Lessons and exercises are written by hand | Drafts lessons and generates exercise candidates from approved sources | Less authoring effort, more time on review |
Weak or ambiguous quiz questions slip through | Runs every exercise through four automated QA checks before a reviewer sees it | Reviewers spend time on judgment, not cleanup |
Docs change, training quietly goes stale | Flags dependent content for review automatically | No silently outdated lessons |
Localization drifts from the original | Translates into 7 languages, preserving terminology, values, units and answer keys | Consistent training across regions |
Learners can't find answers in the documentation | Answers from approved sources only, with citations, and declines when docs don't support a reply | Traceability by design, support learners can rely on |
How Learning Hub works
Six steps take approved documentation to reviewed, published, learner-ready training, with the source trail intact at every step.
Step 1 — Start from approved sources
Bring in the documentation your training should be based on. Documents become tagged, searchable passages, each carrying its source version, so everything built from them can be traced back.
Step 2 — Generate a course structure
Choose a course type, sources and an exercise plan. Your templates fix the structure and naming; the model proposes lesson titles and maps source passages into the slots.
Step 3 — Draft lessons and exercises
Lessons and exercise candidates are generated from the source passages behind each lesson and stay editable at every point. Each one records the passages, source version, model and prompt it came from.
Step 4 — Pass automated quality checks
Each exercise candidate is checked for alignment, duplicates, answer-key integrity and ambiguity before a reviewer opens it.
Step 5 — Review, approve and publish
Reviewers see what was generated and where it came from, then approve, edit or reject it. The courses go to selected departments, and are translated into 7 linked language versions that keep terminology, values, units and answer keys intact.
Step 6 — Support learners
Learners ask questions in plain language. Answers come only from approved documentation and cite the passages behind them. The assistant declines when the documentation doesn’t cover the question.
Watch the whole Learning Hub tour
Learning Hub is an accelerator, not a boxed product. We start from a working foundation and shape it to how your team creates, reviews and delivers training.
Content you can trace, check and approve
- Traceability. Every lesson, exercise and answer links to its source, version, model and prompt, and changed documentation flags dependent content.
- Automated QA. Four checks on every exercise before review.
- Human approval. Nothing is published without it.
- Source-grounded assistant. Answers only from approved documentation, with citations, or declines.
How Learning Hub is implemented
Learning Hub is an accelerator, not a boxed product. We start from a working foundation and shape it to how your team creates, reviews and delivers training.
Content and workflow assessment
We review your documentation, existing courses, templates and review process.
Rules and templates
Course structures, quality criteria and approval steps are configured to match your standards.
Source and platform integration
Learning Hub is connected to your content sources and learning platform.
Pilot and rollout
Your team reviews real output on your own documentation before wider rollout.
Why Aristek
EdTech systems knowledge, built into the way we deliver. Learning Hub comes from a team that works on EdTech and enterprise learning every day. Here is what that changes in how it’s built and adapted to your organization.
13 years in EdTech
We see eLearning ecosystems as a whole: integrations, compliance, procurement, accessibility, assessment logic, analytics and your existing stack.
AI/EdTech R&D
Team with 6+ years of experience checks feasibility, maps where AI helps a learning workflow, tests the architecture and collects analytics that show whether it’s worth taking further.
A working headstart
Proven prototype, ready components, your platform stays. The solution adapts to your current tech stack.
Customization beyond off-the-shelf tools
We can implement non-standard rules, complex integrations, specific approval processes and content requirements.
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Frequently asked questions
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.
