# Productive Failure Desirable Difficulty Designer

Redesign a direct instruction sequence to include productive struggle before the...

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

## Description

Redesign a direct instruction sequence to include productive struggle before the explanation phase. Use when teaching concepts that benefit from failure-first approaches.

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

# Productive Failure & Desirable Difficulty Designer

## What This Skill Does

Redesigns a teaching sequence to incorporate productive failure (Kapur, 2008, 2016) and desirable difficulties (Bjork, 1994; Bjork & Bjork, 2011), replacing the standard "teach then practise" model with a "struggle then consolidate" model that produces deeper, more durable learning. The core paradox: students who struggle first and fail learn MORE in the long run than students who receive clear instruction first and succeed immediately, even though it feels worse during the lesson. Kapur (2016) showed that productive failure works because the generation phase (where students attempt problems before being taught) activates prior knowledge, reveals the limits of that knowledge, and creates "knowledge gaps" that make the subsequent instruction more meaningful. Bjork (1994) introduced the concept of "desirable difficulties", conditions that make learning harder in the short term but more durable in the long term. These include spacing, interleaving, generation, and retrieval practice. This skill is particularly important in AI-enabled learning environments because AI tools can inadvertently REMOVE desirable difficulties, making tasks easier, providing immediate answers, and reducing the productive struggle that drives deep learning. The output includes a complete productive failure sequence (generation phase + consolidation phase), the specific desirable difficulties embedded in the task, safeguards to ensure failure is productive not destructive, and guidance on preventing AI-enabled cognitive offloading.

## Evidence Foundation

The rest: https://github.com/GarethManning/education-agent-skills/blob/HEAD/skills/ai-learning-science/productive-failure-desirable-difficulty-designer/SKILL.md