Works with the AI tools you already use
design learning experiences
A senior learning experience designer for architecting rigorous, evidence-based curricula and educational systems.
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See it in action
You say
Review this draft for a 3-day coding bootcamp. It currently focuses on syntax lectures. I need to ensure students can actually debug a production incident by the end.
Your agent does
The current design confuses content coverage with performance capability. To move from syntax knowledge to debugging judgment, we must reframe the journey.
Proposed Shift:
- Evidence: Students must resolve three tiered 'broken' environments without hints.
- Activity: Replace 2 hours of lectures with 'Live Refactoring' where students justify fix choices.
- Feedback: Implement a peer-review loop focused on error-trace logic rather than just 'working code'.
Which debugging framework should we prioritize for the rubric?
What you get
About this skill
The problem
Instructional design often focuses on content delivery and completion rates rather than actual capability development. Developers and educators frequently struggle to move beyond generic templates to create rigorous, implementable learning systems that bridge the gap between "knowing about" and "doing."
What it does
- Diagnoses and reframes learning briefs to identify root performance problems over simple content needs.
- Architects learning journeys that prioritize active sense-making, practice, and feedback loops.
- Designs assessment strategies that produce credible, authentic evidence of learner capability.
- Evaluates educational technology and AI integration based on pedagogical value and operational feasibility.
- Translates high-level educational strategies into concrete specifications for facilitators and developers.
Why this beats prompting it yourself
Generic prompts result in decorative complexity and "content dumps" that ignore cognitive load and implementation risks. This skill acts as a senior partner, challenging weak assumptions and forcing explicit choices between pedagogical trade-offs that DIY prompts overlook.
Use cases
- Architecting a multi-week technical certification program with integrated assessments.
- Auditing an existing workshop design to identify gaps in guidance and feedback mechanisms.
- Developing an AI-supported learning strategy that preserves human judgment and authorship.
- Creating implementable storyboards and design rationales for complex simulation-based training.
Known limitations
Requires the user to provide specific context regarding learners and constraints to avoid neutral, generalized outputs. It will not blindly follow instructions that result in poor learning outcomes.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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