from claude-education-skills211
Generates a component-level map of where AI assistance supports, is neutral, or undermines learning objectives in an assignment, and produces defensible, compon
AI Learning Boundary Mapper analyses a given classroom assignment and its learning objectives, then produces a component-by-component map identifying where AI assistance is beneficial, neutral, or undermining. The skill outputs clear policy language teachers can drop into assignment briefs, justifications for each restriction, tool-selection guidance (search vs. AI), and practical redesign suggestions to preserve learning-critical challenge while allowing helpful AI uses. It is grounded in backward design and learning-science evidence (Wiggins & McTighe; Bjork et al.; Kirschner et al.).
Use this skill when redesigning assignments for an AI-enabled classroom, when drafting defensible AI-use guidance for a course, or when you need to turn vague "AI allowed"/"no AI" rules into component-specific policies teachers can implement and explain to students. Useful for formative and summative tasks across subjects.
Designed for conversational/code-capable assistants (Claude-like and general-purpose LLM agents) that can accept structured inputs and return markdown outputs. Works well in teacher-support workflows and curriculum-design pipelines.
This skill has not been reviewed by our automated audit pipeline yet.
