disciplinary-ai-literacy-sequence-designer

Community

Teach AI reliability by knowledge type.

AuthorGarethManning
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill helps teachers design a comparison sequence so students learn when AI is likely reliable versus distorted, based on what kind of knowledge different disciplines produce.

Core Features & Use Cases

  • Disciplinary side-by-side comparison: Students compare AI outputs for the same anchor question across two or three subjects to surface differences in reliability.
  • Knowledge-type grounded predictions: The sequence culminates in a predictive framework (e.g., vertical vs horizontal discourse or other knowledge-structure distinctions) rather than a static list of mistakes.
  • Teacher-ready lesson structure: Provides a two-lesson workflow, including what students should record, how to analyze patterns, and a guided discussion with a transfer test.

Quick Start

Use the disciplinary-ai-literacy-sequence-designer skill to produce a two-lesson sequence for students comparing AI answers across the disciplines Biology, History, and Ethics at Year 11 level.

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: disciplinary-ai-literacy-sequence-designer
Download link: https://github.com/GarethManning/education-agent-skills/archive/main.zip#disciplinary-ai-literacy-sequence-designer

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