ai-feedback-design-principles
CommunityAudit and refine AI feedback for learning.
Education & Research#evidence-based#education-research#formative-feedback#ai-feedback-design#pedagogical-design#llm-feedback
AuthorGarethManning
Version1.0.0
Installs0
System Documentation
What problem does it solve?
AI-generated feedback is frequently generic and difficult for educators to translate into actionable improvements; this Skill provides a principled framework to audit, critique, and redesign automated feedback to improve learning outcomes.
Core Features & Use Cases
- Evaluate AI feedback against established research models (Shute 2008; Hattie & Timperley 2007; Narciss 2008) to identify gaps between design and impact.
- Produce an improved feedback design with explicit, actionable recommendations tailored to student level and task type.
- Generate practical implementation guidance and integration steps for real learning systems and platforms.
Quick Start
Provide a short scenario with current feedback; the Skill will output a redesigned feedback design and an implementation plan.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: ai-feedback-design-principles Download link: https://github.com/GarethManning/claude-education-skills/archive/main.zip#ai-feedback-design-principles Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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