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design learning storyboards
Design and audit evidence-informed learning storyboards for digital, blended, and face-to-face instruction.
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See it in action
You say
Review this draft storyboard for a 20-minute module on 'Conflict Resolution for Managers'. Identify where the feedback loop is weak and propose a revised detailed specification.
Your agent does
Diagnostic Audit
- Issue: Feedback is terminal and lacks learner action.
- Consequence: Learners cannot correct misconceptions in real-time.
- Severity: High.
Revised Specification (Block 3)
- Cognitive Operation: Discriminate between de-escalation vs. escalation cues.
- Learner Action: Select response in a branched simulation.
- Feedback Sequence: If incorrect, identify the missed cue, explain the standard, and provide an immediate retry with a varied scenario.
- Confidence: High (Evidence-based retrieval practice).
What you get
About this skill
The problem
Translating a learning map into a functional storyboard often results in "busy work" or passive content that lacks cognitive depth. Designers struggle to align evidence-based learning mechanisms with specific delivery constraints while maintaining clear specifications for writers and developers.
What it does
- Generates high-level learning architectures and detailed activity specifications for digital, blended, or face-to-face delivery.
- Audits existing storyboards against an evidence-informed review rubric to identify gaps in cognitive load management and feedback loops.
- Specifies precise cognitive operations
retrieve,discriminate,inferrather than vague engagement metrics. - Calibrates guidance, scaffolding, and transfer strategies based on learner prior knowledge and target performance conditions.
- Produces implementation-ready briefs including media functions, accessibility requirements, and diagnostic feedback sequences.
Why this beats prompting it yourself
Generic prompts often produce shallow content-heavy slides rather than instructional designs. This skill enforces a rigorous evidence-based framework that justifies every decision with confidence levels and research-backed rationales, ensuring the design actually facilitates learning transfer.
Use cases
- Converting an approved learning brief into a detailed screen-by-screen specification for an e-learning developer.
- Performing a diagnostic audit on a workshop storyboard to identify where learner processing might fail.
- Designing a blended learning module that balances self-paced theory with facilitated practical application.
- Creating a retrieval-heavy revision sequence for a professional certification course.
Known limitations
Does not generate final learner-facing copy, scripts, or full program architectures. Requires an existing learning map or brief for best results.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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