How to turn a policy document into SCORM training

To turn a policy into SCORM training, upload a supported source, write a brief about the decisions learners must make, review the proposed structure, check and edit the generated draft, then export and test the exact package in your LMS. Source text helps guide generation but does not guarantee every policy rule is carried through correctly. A qualified owner must verify legal, safety and local-policy statements before delivery. Start with the creation workflow.

Why a policy PDF doesn't train anyone

A policy PDF is a reference document wearing a training badge. It has three gaps that matter. First, there is no interaction — the learner scrolls, their eyes glaze, and nothing forces a decision. Second, there is no scoring, so you have no evidence anyone understood the part that counts. Third, there is no completion record your LMS can report on. For a compliance topic that last gap is the expensive one: when someone asks you to demonstrate that your workforce was trained on, say, your obligations under the UK GDPR and the Data Protection Act 2018, an email chain of "read and confirmed" replies is not evidence of competence. You need to show who completed the training, when, and that they met a threshold.

What "good" looks like when a policy becomes training

• Scenarios built from real situations the policy governs — not "what does clause 4.2 say", but "a parent emails you a photo of another child; what do you do?" The decision, not the definition, is the learning.
• Scored checks with a pass threshold the LMS can record and report against, so completion means something.
• Per-question feedback checked against the policy — add exact clause references where needed; generated explanations should be verified before use.
• Completion and score signals sent through the SCORM runtime, then confirmed in the receiving LMS before any audit claim.
• Facilitator context kept in authoring but stripped from the learner-facing package, so you can annotate the source of truth without leaking notes into the export.

The honest test is whether a learner leaves able to make the right call under pressure, not whether they can recall a clause number. That is why scenario- and assessment-shaped output beats a slide deck for anything with a real-world consequence — see the scenario-based learning examples for how that plays out in practice.

Step 1 — Upload the policy as a source

On the compliance training generator, attach a supported policy document or source URL. The source-extraction path supports PDF, DOCX, PPTX and plain text, subject to current file and access limits shown during upload. A private intranet page may not be reachable by the extractor. Inspect the extracted source and the generated draft: attaching a policy does not guarantee correct quotation, coverage or citation.

Step 2 — Write a brief that names the audience and the behaviour

The brief is the single biggest lever you have, and you get up to 5,000 characters for it. The mistake is to describe the topic: "make training on our data protection policy" gives the model nothing to aim at. Instead, name the audience and what they must be able to *do*. Who is this for — front-line staff, line managers, a specific department? And what decision should they get right afterwards? Be concrete about the behaviours you are worried about, because those are the moments the scenarios should recreate.

Step 3 — Review the proposed structure before you generate

Intle detects an initial content type and offers a proposed structure before full generation. Check whether the sections cover the decisions in your policy and place practice early enough. Adjust the plan or choose an assessment or scenario format where appropriate. The detected type and proposed outline are suggestions for review, not a policy interpretation.

Step 4 — Generate, then edit what it got almost right

Generation runs in the background; processing time varies with source length, output depth, and service load. Under the hood, quality gates check the output before it reaches you — interactivity ratio, visual density, the longest run of non-interactive screens, and variety in how questions are answered. What arrives is a draft, not gospel. Open it in the editor and fix the things a model can only get *almost* right: a scenario that needs your organisation's real reporting line, a feedback note that should quote your exact policy wording, or a threshold that should match your internal standard. This is where your subject-matter expertise earns its place.

Step 5 — Export SCORM or host a session

When it is right, export it. Intle packages target SCORM 2004 4th Edition and SCORM 1.2. The public matrix identifies the exact build behind dated package-level checks and keeps named platforms pending until tested. Check /lms-compatibility, import the package into your exact LMS configuration, and verify launch, score, completion and resume before rollout. If you would rather skip the LMS, host it as a session link instead.

A worked example: a data-protection policy

For example, a school might attach its data-protection and acceptable-use policies, then ask for a draft in which office staff decide how to respond to a suspicious request for pupil data. The school phishing example shows a related stored learner experience; it was not generated from that hypothetical policy. Use it to inspect interaction shape, then verify the draft against the real policy and current law. Compliance starters provide editable briefs if no source document is ready.

Keep the policy version, review decision and package build with the deployment record. The LMS may report a completion or score after a successful import and learner attempt; those signals need checking and do not by themselves establish competence or legal compliance.