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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 is a well-structured educational skill that helps teachers map which assignment components benefit from vs. are undermined by AI assistance. It's a pure prompt-based skill with no scripts — just a detailed SKILL.md with frontmatter, input/output schemas, evidence citations, and an extensive example output. The skill has strong academic grounding and clear instructions, but its audience is niche (teachers designing AI-era assignments) and it requires no technical setup beyond invoking the prompt.
Pure prompt skill with no executable code. No security concerns whatsoever. Well-documented with academic references and clear input/output contracts. Niche but well-executed for its domain. Architecture follows the skill spec reasonably well with frontmatter, schemas, and example output, though it embeds the full prompt in SKILL.md rather than separating into references/.
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