SAWI — AI-Assisted Course Design for Professional Certification

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SAWI — AI-Assisted Course Design for Professional Certification

{{PLACEHOLDER: One-line principle. A bold statement about curriculum design — e.g. “A curriculum is not a slide deck. It is a system that turns beginners into practitioners — and the design of that system is the real expertise.“}}

The challenge

{{PLACEHOLDER: The market problem.

Suggested angle: Professional training programmes face a structural tension — they must cover mandated content (federal diploma requirements) while producing practitioners who can actually do the work. Most course design defaults to content delivery (slides, readings, exams) because it is easier to build than genuine competency progression.

Frame this as the problem institutional training clients face: compliance versus capability.}}

The approach

{{PLACEHOLDER: Your methodology.

What to cover:

  • How you design curricula in Obsidian (structured notes, interconnected modules, progression logic)
  • The AI-assisted workflow (how Claude / LLMs help with content architecture, not just content generation)
  • Learning science principles applied (scaffolding, retrieval practice, desirable difficulties, interleaving)
  • How you balance federal diploma requirements with practical skill building
  • The role of comprehension gates, assessments, or competency checks in the design

This section should demonstrate: you design learning systems grounded in evidence, not just content collections.}}

The build

{{PLACEHOLDER: What was created.

Specifics to include:

  • The programme structure (modules, progression, duration)
  • The Obsidian-based design system (how the curriculum is architected as connected notes)
  • Specific examples of AI-assisted design decisions
  • Assessment design
  • Any tools, templates, or reusable frameworks created
  • The scale (number of students, cohorts, modules)}}

The impact

{{PLACEHOLDER: Results and current state.

What to include:

  • Student outcomes (pass rates, competency metrics, qualitative feedback)
  • Programme adoption or continuation
  • Efficiency gains from the AI-assisted design process
  • Institutional feedback
  • What you would change or what is evolving}}

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