# AI Learning Boundary Mapper

Map which elements of an assignment benefit from AI assistance vs.

- Repository: https://github.com/GarethManning/education-agent-skills (939 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-10-22
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add GarethManning/education-agent-skills --skill ai-learning-boundary-mapper -g -a claude-code -y`
Codex: `npx skills add GarethManning/education-agent-skills --skill ai-learning-boundary-mapper -g -a codex -y`
Cursor: `npx skills add GarethManning/education-agent-skills --skill ai-learning-boundary-mapper -g -a cursor -y`
Gemini CLI: `npx skills add GarethManning/education-agent-skills --skill ai-learning-boundary-mapper -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add GarethManning/education-agent-skills`

## Description

Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# AI Learning Boundary Mapper

## What This Skill Does

Generates a component-by-component analysis of a specific assignment, mapping which elements benefit from AI assistance, which are neutral, and which are undermined by AI involvement, based on the learning objectives the assignment serves. This is the teacher-facing design tool for AI-age assignment redesign: it takes an existing assignment and produces a boundary map that allows teachers to set specific, defensible AI use policies rather than blanket "AI allowed" or "no AI" positions. The central insight is that within any single assignment, different components serve different learning objectives, and AI assistance that helps with one component may undermine another. An essay that requires both research (AI can assist with summarising context) and original argumentation (AI assistance bypasses the cognitive work of constructing an argument) benefits from a component-level policy, not a uniform one. The output includes an objective analysis (for each learning objective, whether AI assistance supports or undermines it), a component boundary map, defensible AI policy recommendations, an optional Google vs. AI chatbot tool comparison for information-gathering tasks, and redesign suggestions that preserve learning-critical challenge while permitting AI use where it genuinely helps. This skill is the teacher-design complement to `metacognitive-monitoring-ai-contexts`: boundary-mapping prevents the metacognitive risk from arising; `metacognitive-monitoring-ai-contexts` addresses it when it does.

## Evidence Foundation

The rest: https://github.com/GarethManning/education-agent-skills/blob/HEAD/skills/ai-literacy/ai-learning-boundary-mapper/SKILL.md