AI for learning design: a hands-on course for in-house L&D teams

Why attend this course?

Design better, not just faster

Move beyond AI as a content generator. Use it to improve quality from the start, including learning system architecture.

From prompt user to system designer

Learn to design AI-assisted learning environments rather than just using AI tools. A different and more future-proof skill.

Build repeatable AI-assisted workflows (with human review and governance)

Leave with a team-ready AI workflow covering every stage of the development lifecycle, not isolated prompts.

Stay in control

Learn how Human-in-the-Loop architecture can be applied to both production tasks and agentic systems where AI can act autonomously.

Work with real tools

Hands-on practice with general-purpose LLMs and specialist learning production tools.

Stay tool-agnostic

Frameworks that work regardless of which LLM or authoring platform your organisation uses, now or in future.

Built for your team

Private sessions tailored to your sector, tools, and internal scenarios before delivery.

A resource pack you can use

Delegates will get a resource pack that includes:

• An eval prompt template
• A reusable prompt structure you can adapt to any content type
• A rubric starter kit
  • Pre-built rubric criteria for the three most common L&D content types
• A one-page eval plan

Examples of using AI as for elearning content

Rapid development of online exercises

We’ve used AI to build over 120 online exercises for our elearning courses.

Below are some examples:

• Policy editing challenge
  • Review a poorly edited workplace policy/procedure document. Highlight problem sections and classify the issue type.
• Exercise on the five-stage writing process
  • Place each technical writing activity into the stage where it best belongs.

Creating less generic, more personalised, corporate training videos

This is an example of how AI videos can be edited – in this case, changing the voice and background to different corporate offices. This capability can used by corporate Learning and Development teams to create less generic elearning or training video content.

Topics covered

Note: this outline is subject to change based on participant needs and context.

AI foundations for L&D practitioners

Reframing AI in the context of corporate learning, including the shift from tools to agents.

• How LLMs work (what L&D practitioners actually need to know)
• Overview of the Al tools relevant to corporate L&D
• Common failure modes
  • Where Al produces plausible but poor learning design
• Introduction to Human-in-the-Loop architecture
• What changes when Al can take sequences of actions (Agents)
  • Skills v. general-purpose Al
  • Scoped, reusable agents at organisational scale
  • What this means for the L&D role (from content producer to learning system architect)
• Having the Al conversation with stakeholders and senior leaders

Needs analysis and learning design with AI

Turning briefs into architecture, and designing for learners who co-author their own paths.

• Turning a vague stakeholder brief into a structured needs analysis
• Al-assisted discovery question generation and SME interview preparation
• Writing and stress-testing learning objectives with Al
• Generating and refining course structures and module outlines
• Building a design rationale document for stakeholder sign-off
• Agentic needs analysis
  • Designing agents that conduct SME interviews, synthesise data, and surface emerging skill gaps
• Learner-initiated learning paths
  • Frameworks for self-directed, Al-assembled curricula without losing coherence

Content development: advanced workflow automation

Building AI-assisted workflows for the full range of corporate learning content.

• Scripting and storyboarding with Al: prompting, editing, and quality control
• Scenario and case study development with Al assistance
• Facilitator guides, job aids, and performance support materials
• Automating repetitive development tasks: versioning, formatting, translation preparation
• Prompt engineering for L&D: a practical framework with ready-to-use templates
• Dynamic and evergreen content: building modular, source-linked content that an agent can keep current
• Assisting v. delegating: when to work with Al and when to hand off to an agent
• Democratised content creation: governance templates and approval workflows for team-lead-built learning
• Agent-orchestrated pipelines: how script → voice → video → packaging can run with minimal manual intervention
• Agent-assisted video editing: using AI to plan edits, add b-roll, apply branded templates, create preview files, incorporate feedback, and render final e-learning videos
• Human-in-the-loop review for AI-edited media: checking accuracy, accessibility, visual quality, tone, and instructional relevance before publication

Specialist multimedia tooling

Using specialist AI tools for audio, video, image, and interactive content within a coherent production workflow.

• AI narration and voice: tools, use cases, and quality standards
• AI video and presenter tools for corporate learning contexts
• Image and graphic generation for learning materials
• Integration with common authoring tools
• Building a multimedia workflow that combines LLMs and specialist tools

Assessment and evaluation design with AI

Using AI to design assessments that actually measure learning – and evaluation frameworks that demonstrate impact to the business.

• Scenario-based and knowledge-check assessment design with AI
• Avoiding common AI assessment pitfalls (surface recall, lack of transfer)
• AI-assisted evaluation planning at Kirkpatrick levels 1–3
• Generating learner feedback surveys and post-training reinforcement content
• Conversational and adaptive assessment: designing for agents that quiz and adapt in real time
• Richer learning data: what agentic interactions reveal that completion rates cannot
• Al as ongoing performance support: designing for reinforcement, recall, and in-the-flow-of-work learning

Governance, quality control, and ethics

Frameworks that keep AI-assisted and AI-mediated learning trustworthy and compliant

• Reviewing Al-generated content: a practical QA checklist for L&D
• Data privacy, IP, and confidentiality in Al-assisted workflows
• Bias, accuracy, and hallucination: what to watch for and how to catch it
• Building internal Al usage guidelines your team will actually follow
• Using Al evals for courseware quality assurance
• Governing agentic systems: accountability when Al autonomously updates content or advises learners
• Transparency with learners: disclosure obligations when an agent is part of the learning experience
• The skills v. knowledge question: what still needs to be genuinely learned vs. looked up on demand

Building your team’s AI workflow

A repeatable, team-wide approach, and rethinking the L&D operating model.

• Designing an end-to-end Al-assisted development process for your team
• Roles, responsibilities, and Human-in-the-Loop decision points
• Change management: bringing sceptical colleagues and risk-averse stakeholders along
• Measuring time and quality impact – making the case internally
• Keeping up as the tools change: a system for staying current
• Redesigning the L&D operating model: which activities require humans, which can be delegated to agents
• Designing for a two-tier learner: those confident with self-directed Al learning and those who need structured support
• The always-on learning environment: L&D’s role when agents are embedded in work tools 24/7

Designing agentic learning systems

A dedicated session for teams ready to design with and for agents, rather than just using AI in existing processes.

This session can be delivered as a optional final module, or as a standalone follow-on for those who have covered the foundations elsewhere.

• What makes a learning experience “agentic”? Design principles and patterns
• Mapping the learner journey when Al agents are active participants
• Designing SME knowledge-elicitation agents (structure, prompting, and synthesis)
• Building self-directed learning environments (scaffolding without over-constraining)
• Simulation at scale:
  • Agent-generated branching scenarios and adaptive case studies
• Coaching augmentation
  • Designing agents that reinforce workshop learning between sessions
• Measurement and attribution in agentic environments
  • Tracking what actually happened

Pricing

Corporate / in-house sessions

This course is available exclusively as a private session for in-house teams. Pricing is based on group size and any customisation required.

Private sessions include:

• A dedicated session for your organisation only
• Content and exercises tailored to your industry, tools, and learning context
• Use of your own internal scenarios and examples where appropriate
• Flexible scheduling to suit your team across time zones
• A participant workbook and prompt library to take away
• Optional follow-up consultation session

Contact us to discuss your requirements and receive a proposal.