A SCORM localisation pipeline that turned weeks into hours
Localising a course used to mean rebuilding it from scratch, one language at a time. I built a workflow in Claude that does the heavy lifting and keeps a designer in charge of the parts that matter.
The problem
CIPS serves a global membership, and its learning needs to reach professionals in more than one language. Getting there was painfully slow. For every language, a designer effectively rebuilt the module from scratch: exporting the text, re-recording the narration, dropping it all back into the authoring tool, and re-testing the SCORM package. It was expensive, it did not scale, and it quietly turned multilingual delivery into something the team avoided rather than offered.
The learner and the constraints
The people at the end of this are expert, time-poor professionals who expect content in their own language and to a professional standard. Nothing about being fast could change that. Every localised module still had to be accurate, on-brand, accessible and safe to publish under a chartered body's name. Machine translation on its own was nowhere near trustworthy enough for that. Speed could not come at the cost of quality, so I designed for both.
The question I set myself: how do I make localisation an order of magnitude faster without taking the human judgement out of it?
The approach, and why
I did not treat AI as a translate button. I built a governed pipeline in Claude that takes on the mechanical work and leaves the decisions to a designer:
- Ingest. The pipeline reads an existing SCORM module and extracts its text, structure and metadata into a reviewable form.
- Translate in context. A custom AI skill translates the content with instructional intent preserved, using CIPS terminology and register rather than generic translation.
- Human-in-the-loop review. A designer checks accuracy, tone, terminology and cultural fit before anything is rebuilt. This gate is never skipped.
- Reassemble. The approved content is repackaged into a production-ready, SCORM-compliant module that drops back into the LMS.
- QA and accessibility check. Final cross-platform testing confirms the module works and still meets WCAG 2.2 AA.
Artefacts
Pipeline architecture diagram, a sample before-and-after module, and the custom AI skill definition are available to walk through on request. (Live demo and screenshots to be embedded here.)
The outcome
The pipeline brought localisation from weeks down to hours. Multilingual delivery went from something the team avoided to something it could offer as standard, and every published module still had a designer's name on the sign-off.
What I took from it
The value was never the translation. It was the governance around it. Building the human review gate in from the start is the only reason this was ever safe to use on regulated content. And the time it saved did not disappear into doing more, faster. It went back into analysis, evaluation and design, which is where a designer actually earns their keep.
Further reading: the governance principles behind this pipeline, and why AI should support, not replace, the designer.