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ai-learning-boundary-mapper

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Set defensible AI rules per assignment task.

Education & Research#learning objectives#assessment alignment#backward design#ai policy#ai-literacy#assignment design
AuthorGarethManning
Version1.0.0
Installs0

System Documentation

What problem does it solve?

It helps teachers avoid blanket AI “allow” or “ban” policies by mapping where AI helps learning and where it undermines the specific cognitive work an assignment is meant to develop.

Core Features & Use Cases

  • Component-level AI boundary mapping: Breaks an assignment into learning-relevant components and labels each as AI-beneficial, AI-neutral, or AI-undermining.
  • Objective-aligned analysis: Connects AI impact directly to each stated learning objective using backward design (Stage 1 → Stage 2 → Stage 3).
  • Defensible, student-facing AI policy: Produces an assignment-ready AI use statement plus rationales for any restrictions.
  • Information-tool comparison: When needed, differentiates search vs. AI chatbot behavior so students use the most epistemically appropriate tool for each information task.

Quick Start

Use the ai-learning-boundary-mapper skill with an assignment description and learning objectives to get a component-by-component AI policy statement for that specific task.

Dependency Matrix

Required Modules

None required

Components

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-learning-boundary-mapper
Download link: https://github.com/GarethManning/education-agent-skills/archive/main.zip#ai-learning-boundary-mapper

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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