Draft first-pass scripts, scenarios and assessment items from subject-matter expert source material using generative AI.
Human Resources & Personal Development
Using Generative AI to Draft, Localise and Update Learning Content
Shows learning and development teams how to use generative AI to draft first-pass content, localise it for new markets and keep it current, while keeping a human editorial and fact-checking layer over every output.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Learning teams are expected to produce more content, in more languages, updated more often, without a matching increase in instructional design headcount. Generative AI can draft a first-pass script from a subject-matter expert's notes, translate and adapt a module for a new market, and rewrite a policy-heavy course the week regulation changes, but only if someone designs the workflow so speed does not come at the cost of accuracy or brand voice. This course gives instructional designers and L&D managers a practical method for using AI across the content lifecycle: drafting scenarios and assessment items from source material, localising content so it reads as if written for that market rather than translated into it, and updating existing courses without rebuilding them from scratch. Participants build a prompt library tied to their own style guide, practise fact-checking AI output against source documents to catch confident-sounding errors, and design an editorial review step that fits real production deadlines. The course also covers the data protection and intellectual property questions that arise when proprietary content and SME knowledge go into an AI tool. Participants leave with a content-update workflow, a localisation quality checklist and a reusable prompt library.
Expected Learning Outcomes
Build a prompt library that encodes a house style guide so AI output needs less rewriting to sound on-brand.
Localise learning content for new markets so it reads as written for that audience, not mechanically translated.
Fact-check AI-generated content against source documents to catch hallucinated facts, figures and policy detail.
Design an editorial review workflow that fits AI drafting into existing production deadlines and sign-off steps.
Update existing courses for policy or product change by editing AI-assisted drafts rather than rebuilding from scratch.
Apply data protection and intellectual property safeguards when uploading proprietary content to AI tools.
Who Should Attend
Instructional designers and e-learning developers producing content under tight production deadlines.
L&D managers responsible for keeping compliance and product training current across markets.
Localisation and translation specialists adapting training content for new regions and languages.
Learning technologists selecting and configuring generative AI tools for content production.
Subject-matter experts who co-author training content alongside instructional design teams.
Training content agencies and freelance designers producing courses for multiple clients.
Course Modules
Select any module to see its sessions and points.
01Generative AI Across the Content Lifecycle
2 sessions · 8 points
Session 1Where AI Fits in Instructional Design
- Map the content lifecycle from source material to published course and identify where AI drafting saves the most design time.
- Compare AI-assisted drafting against models such as ADDIE and the Successive Approximation Model to keep design rigour intact.
- Distinguish tasks suited to AI drafting, such as first-pass scripts and quiz items, from tasks that still need a human designer.
- Set realistic expectations with stakeholders about how much editorial time an AI-assisted draft still requires.
Session 2Turning SME Material into a Usable Draft
- Extract structured content from subject-matter expert interviews, transcripts and existing documents to brief an AI drafting tool.
- Generate branching scenarios and case discussions that translate technical source material into practical, memorable situations.
- Write assessment items aligned to stated learning objectives using recognised question-design principles.
- Flag source material that is too ambiguous or incomplete for AI drafting to be useful without further SME input.
02Building a Prompt Library and Style Guide
2 sessions · 8 points
Session 1Encoding Brand Voice and Structure into Prompts
- Translate a written style guide into reusable prompt instructions covering tone, terminology and formatting.
- Build a prompt library organised by content type, such as scenario, assessment item, video script and job aid.
- Test prompts against known-good examples to check consistency before rolling them out to a wider design team.
- Version-control prompts so improvements are shared across the team rather than kept in individual chat histories.
Session 2Generating Accessible and Well-Structured Output
- Prompt for accessibility features, including plain-language phrasing, descriptive alt text and caption-ready narration scripts.
- Structure AI output so it maps cleanly onto existing templates, slide layouts and content management fields.
- Generate multiple format variants, such as a short job aid and a full module, from the same source content.
- Review generated structure against a basic accessibility checklist before content moves into production.
03Localising Content for New Markets
2 sessions · 8 points
Session 1Beyond Word-for-Word Translation
- Distinguish translation from localisation and identify which elements, such as examples, currency and imagery, need cultural adaptation.
- Use AI to produce a first-pass localised draft, then apply a review step with a native-speaking reviewer for tone and idiom.
- Identify content that carries legal or regulatory meaning and route it for specialist human translation rather than AI alone.
- Adapt scenario-based content so situations and names feel authentic to the target market rather than obviously translated.
Session 2Quality Assurance for Localised Content
- Build a localisation QA checklist covering terminology consistency, formatting, date and currency conventions and tone.
- Run back-translation spot checks on high-risk content, such as safety or compliance material, to confirm meaning held.
- Track localisation turnaround time and rework rate to show where AI assistance is genuinely saving time.
- Maintain a market-specific glossary that keeps terminology consistent across future localisation projects.
04Updating Content, Fact-Checking and Governance
2 sessions · 8 points
Session 1Keeping Courses Current Without Rebuilding Them
- Use AI to identify sections of an existing course likely affected by a policy, product or regulatory change.
- Generate updated drafts of only the affected sections while preserving the surrounding content and structure.
- Fact-check updated content line by line against the current source of truth before republishing.
- Log content updates and their trigger, such as a policy change, so audit trails show why a course changed.
Session 2Data Protection, IP and Disclosure
- Assess what proprietary content and personal data are safe to enter into a generative AI tool under company policy.
- Clarify intellectual property ownership of AI-assisted content created under vendor and platform terms of use.
- Decide when to disclose AI involvement in content creation to learners, stakeholders or regulators.
- Build a vendor due-diligence checklist for AI content tools covering data handling, retention and training-data use.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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