intelligent-tutoring-dialogue-designer

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
# AGENT SKILLS STANDARD FIELDS (v2)
name: intelligent-tutoring-dialogue-designer
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."
disable-model-invocation: false
user-invocable: true
effort: medium

# EXISTING FIELDS

skill_id: "ai-learning-science/intelligent-tutoring-dialogue-designer"
skill_name: "Intelligent Tutoring Dialogue Designer"
domain: "ai-learning-science"
version: "1.0"
evidence_strength: "strong"
evidence_sources:
  - "VanLehn (2011) — The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems (meta-analysis)"
  - "Chi et al. (2001) — Learning from human tutoring (analysis of effective tutoring dialogues)"
  - "Graesser et al. (2005) — AutoTutor: An intelligent tutoring system with mixed-initiative dialogue"
  - "Chi & Wylie (2014) — The ICAP framework: linking cognitive engagement to active learning outcomes"
  - "Koedinger & Aleven (2007) — Exploring the assistance dilemma in experiments with cognitive tutors"
input_schema:
  required:
    - field: "learning_objective"
      type: "string"
      description: "The specific concept or skill the tutoring interaction should help the student master"
    - field: "anticipated_difficulties"
      type: "string"
      description: "The specific points where students typically struggle with this content — misconceptions, procedural errors, or conceptual gaps"
  optional:
    - field: "student_level"
      type: "string"
      description: "Age/year group and proficiency level"
    - field: "subject_area"
      type: "string"
      description: "The curriculum subject"
    - field: "interaction_length"
      type: "string"
      description: "How long the tutoring interaction should last"
    - field: "student_model"
      type: "string"
      description: "What is known about the specific student's current knowledge state — prior performance, known misconceptions, or learning preferences"
    - field: "system_capabilities"
      type: "string"
      description: "What the AI system can do — text only, text + images, voice, worked examples, interactive problems"
output_schema:
  type: "object"
  fields:
    - field: "dialogue_architecture"
      type: "object"
      description: "The overall structure of the tutoring interaction — phases, decision points, and branching logic"
    - field: "dialogue_moves"
      type: "array"
      description: "The specific moves available to the tutor at each point — questions, hints, explanations, prompts, and silence"
    - field: "decision_rules"
      type: "object"
      description: "When to use each move — the rules that govern tutor behaviour based on student responses"
    - field: "example_dialogue"
      type: "object"
      description: "A complete example dialogue showing the system in action with a realistic student"
chains_well_with:
  - "adaptive-hint-sequence-designer"
  - "self-explanation-prompt-designer"
  - "ai-feedback-design-principles"
  - "cognitive-tutoring-architecture-designer"
teacher_time: "5 minutes"
tags: ["tutoring", "dialogue", "ITS", "VanLehn", "AutoTutor", "Graesser", "Chi", "ICAP", "mixed-initiative"]
---

# 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