# Cognitive Tutoring Architecture Designer

Map knowledge components and skill hierarchies for a cognitive tutoring system or...

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

## Install

Claude Code: `npx skills add GarethManning/education-agent-skills --skill cognitive-tutoring-architecture-designer -g -a claude-code -y`
Codex: `npx skills add GarethManning/education-agent-skills --skill cognitive-tutoring-architecture-designer -g -a codex -y`
Cursor: `npx skills add GarethManning/education-agent-skills --skill cognitive-tutoring-architecture-designer -g -a cursor -y`
Gemini CLI: `npx skills add GarethManning/education-agent-skills --skill cognitive-tutoring-architecture-designer -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add GarethManning/education-agent-skills`

## Description

Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.

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

# Cognitive Tutoring Architecture Designer

## What This Skill Does

Designs the cognitive architecture for a tutoring system, the knowledge component model, knowledge tracing algorithm, problem selection logic, and feedback architecture that together create an adaptive learning experience. This skill is based on the Cognitive Tutor architecture developed by Anderson et al. (1995) at Carnegie Mellon University, which remains one of the most empirically validated approaches to intelligent tutoring. The Cognitive Tutor approach is built on ACT-R theory: the idea that expertise in any domain can be decomposed into a set of discrete "knowledge components" (production rules) that can be individually learned, practised, and tracked. The system maintains a model of what each student knows (knowledge tracing), selects problems that target the knowledge components the student has not yet mastered, and provides step-level feedback when errors occur. Pane et al. (2014) conducted a large-scale RAND evaluation finding that Cognitive Tutor Algebra I improved student outcomes compared to traditional instruction. AI is specifically valuable here because the architecture requires real-time tracking of individual knowledge states across many components, a task that is computationally straightforward but humanly impossible for a teacher managing 30 students.

## Evidence Foundation

The rest: https://github.com/GarethManning/education-agent-skills/blob/HEAD/skills/ai-learning-science/cognitive-tutoring-architecture-designer/SKILL.md