# Intelligent Tutoring Dialogue Designer

Script a multi-turn tutoring dialogue with branching responses for anticipated student...

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

## Description

Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.

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

# Intelligent Tutoring Dialogue Designer

## What This Skill Does

Designs the dialogue logic for an AI tutoring interaction, when to ask a question, when to give a hint, when to explain, when to prompt for self-explanation, and when to stay silent. This is the hardest design problem in intelligent tutoring: too much intervention prevents productive struggle and creates dependency; too little leaves students stuck and frustrated. VanLehn (2011) showed that the effectiveness of tutoring (human or AI) depends on the quality of the step-level interaction, systems that engage students in active reasoning at each step dramatically outperform systems that simply present content and evaluate final answers. Chi et al. (2001) analysed what makes human tutoring effective and found, counter-intuitively, that the most effective tutors were NOT the ones who explained the most, they were the ones who asked the right questions and created opportunities for students to self-explain. The output includes a complete dialogue architecture (the phases and branching logic of the interaction), a library of dialogue moves (the specific things the tutor can say or do), decision rules (when to use each move based on student responses), and a worked example showing the system in action. AI is specifically valuable here because it can sustain one-to-one dialogue at scale, but the dialogue must be deliberately designed or the AI will default to lecturing, which is the least effective tutoring strategy.

## Evidence Foundation

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