For course authors & methodologists

Build training like a product: versioned, reviewed, tested.

Materials scattered across documents, labs nobody tried before the class, and review feedback lost in email threads. classroom.now gives authors one builder for content, assessments and lab infrastructure — with versions, approvals and a preview of exactly what students will see, from first draft to a published catalog listing.

Structured authoring

A block editor that understands how training is structured

A course in classroom.now has a clear backbone: program → course → version → module → lesson → block. You build it in a modern block editor — drag the module tree, insert blocks with “/”, undo any step.

15 block types: video, code editor, web terminal, remote desktop, quiz blocks, whiteboard and VR environments — each validated per type.
Blocks can be targeted per device — desktop, tablet or mobile.
Module release rules: immediately, at a date, as a relative offset, after completing the prior module, or once a minimum score is reached.
A central asset library with usage tracking across courses and protection against deleting files that are in use.
Programs sit above courses: methodologists compose multi-course programs with prerequisites, sequencing and target skills.
A preview of exactly what students will see, at any point while you work — no surprises at publish time.

Versioning & review

Versions and reviews, pull-request style

A new course version clones the entire tree of modules, lessons, blocks, labs and assessments, so you work safely alongside the live version. Publishing then flips the current version atomically — in one moment, with a changelog.

A fixed Draft → In Review → Approved → Published pipeline, with version history, diffs and rollback.
Reviewers claim requests from a prioritized queue and comment right on a specific lesson, block or lab artifact — threaded comments (issue, suggestion, praise, question) rendered as inline markers.
A dual approval track: content review plus a separate infrastructure review for courses with hands-on labs.
Decommission older versions without affecting existing student enrollments.
Content analytics show where students struggle and where they drop off — precise input for the next version.

Co-authoring

Write as a team, without overwriting each other

Lesson-level locks make sure only one author works in one place at a time — human or AI. Version conflicts and lost paragraphs end here.

The editor shows who holds which lesson — across the whole version tree at a glance.
Locks renew themselves while you work and auto-expire after inactivity — nothing stays locked forever.
Need a lesson a colleague is working on? Request the lock release right from the editor — politely, no phone calls.
AI-driven edits take their own distinct lock type — you always see where AI, not a person, is at work.

AI co-creation

AI that proposes. You decide.

The AI co-author works in two modes: inline in your text as a suggester, or in a dedicated panel that generates complete pieces for your review. It understands course structure — and never acts without you.

Copilot-style ghost text: a suggested continuation appears inline and Tab accepts it.
The panel generates a complete lab exercise — instructions, verification script and hints — and whole batches of assessment questions to review and insert.
On demand it improves text, suggests lesson structure or summarizes learner feedback from past runs.
Nothing publishes itself: a human approves every proposal in the normal editors, and AI edits hold their own lock type.
Methodologists tune AI behavior per training type — a pedagogical configuration layer, on top of your organization's budgets and keys.

Assessments & objectives

Assessments wired to learning objectives

Define what the course should teach — and the platform makes sure you actually test it. From question pools to skill mapping, one builder holds it all together.

Learning objectives with Bloom's-taxonomy levels, mapped to skills and to specific assessments — with automatic warnings for objectives no assessment covers.
Question pools drawn and randomized server-side; the answer key never reaches students.
Assessment policies — passing scores, retake limits and waiting periods, time limits — merge through a five-level cascade from platform down to the individual assessment.
Auto-gradable questions grade instantly; free-text answers route to AI evaluation or a manual grading queue with points and comments.
Courses declare which skills they teach and require — your mapping then powers learner skill profiles and managers' gap analysis.

Hands-on labs

You write labs — and test them — like code

You attach real infrastructure to a course version: VMs from Ansible playbooks and templates, Kubernetes environments from Helm charts, VR environments, and externally registered machines. Exercises are then verified by a script, not a hunch.

Resource specs, per-student instance counts and health checks are part of the lab definition itself.
Playbooks are lint-checked server-side, and any infrastructure change sends the version back to infrastructure review — it cannot publish without that approval.
A dry run spins up a throwaway instance where the reviewer actually tries the lab — before it ever publishes.
Your verification scripts power the student's “Check my work” button: they run inside the student's own instance and return a result with output and optional AI feedback.
Finished labs are then provisioned for participants automatically 24 hours before each session.

Publishing

From approved version straight to the catalog

Once both the content and infrastructure reviews approve a version, you publish it in one step — and can list the course in the public catalog on classroom.now, with a provider profile and reviews from real graduates.

In-catalog purchasing: coming

Publishing flips the course's current version atomically — with no impact on existing student enrollments.
The public syllabus shows module and lesson titles only; your block content never goes public.
Every public listing passes platform moderation, and providers can earn a verification badge.
Only someone with a verified completion can review your course.
In-catalog purchasing is coming — today an inquiry form connects interested customers with you.

A day with classroom.now

An author's day, from draft to publish

9:00 — Morning draft

You open the builder and see who holds which lesson. You lock yours, write — and accept suggested continuations with Tab.

11:00 — A new exercise

The AI panel generates an exercise with instructions, a verification script and hints. You review, tweak, insert — the playbook passes lint and a dry run proves it on a throwaway instance.

14:00 — Assessment & objectives

You assemble the final assessment from question pools and map the learning objectives. The platform flags one uncovered objective — you add a question and you're done.

16:00 — Off to review

You submit the version for approval. The reviewer comments right on the blocks, you address them — and once content and infrastructure both approve, you publish to the catalog.

FAQ

What authors ask most often

Can several authors work on one course at the same time?

Yes. Lesson-level locks ensure only one author works in a given lesson at a time. The editor shows who holds what across the whole version tree, locks renew themselves while you work and expire after inactivity. You can request a lock release from a colleague right in the editor — and AI edits take their own, visibly distinct lock type.

What happens to students mid-course when I publish a new version?

Nothing unexpected. Publishing atomically flips the course's current version, and the new version becomes the default from that moment. You can later decommission older versions without affecting existing student enrollments — and every version carries a changelog, so what changed is always traceable.

Can AI-generated content reach students without review?

No. AI proposals are never auto-applied — each one passes through the author's hands in the normal editors, and AI edits hold their own lock type, so it's always visible where AI worked. On top of that, a published version has passed the standard review workflow. And AI runs under your organization's budgets and API keys.

How does a lab I wrote get reviewed?

Courses with labs have a dual approval track: a content review and a separate infrastructure review. Playbooks are lint-checked server-side, the reviewer runs the lab on a throwaway dry-run instance, and any infrastructure change resets that review — without its approval, the version won't publish.

How do I know the assessment actually covers what the course teaches?

Learning objectives carry Bloom's-taxonomy levels and map to skills and to specific assessments. If any objective is verified by no assessment, the platform warns you automatically — you catch the gap while authoring, not after the course's first run.

Bring one of your courses. We'll show you what it looks like in classroom.now.

Tell us what you train today and where you author your content — we'll prepare a walkthrough of the builder, the review workflow and the lab pipeline on your own material.