planning-adaptive-instruction

Generate adaptive classroom session plans from class-wide knowledge state data.

13|Updated Feb 12, 2026

Search Tags:#planning #analytics #kst #knowledge-space #adaptive-instruction #udl-3.0

Ready to run

npx skills add https://github.com/vanderbilt-data-science/knowledge-spaces --skill planning-adaptive-instruction

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: planning-adaptive-instruction
Source: https://github.com/vanderbilt-data-science/knowledge-spaces/tree/main/.claude/skills/planning-adaptive-instruction
Command: npx skills add https://github.com/vanderbilt-data-science/knowledge-spaces --skill planning-adaptive-instruction

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Plan and optimize classroom sessions by analyzing aggregate student states and competence profiles to identify optimal targets, groupings, and peer tutoring pairings within Knowledge Space Theory.

Core Features & Use Cases

Analyzes class-wide knowledge states to generate targeted instructional plans, grouping students by competence and mastery gaps, and suggesting peer tutoring pairings following UDL 3.0.
Integrates with kst_utils analytics and graphs/*.json to produce actionable session plans.
Use Case: A faculty wants to plan a 60-minute lecture targeting items with high target scores and appropriate prerequisites.

Quick Start

Provide a knowledge graph file in graphs/*.json and a session duration to generate a complete adaptive instruction plan.

Frequently Asked Questions about planning-adaptive-instruction

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I plan adaptive classroom sessions from aggregate student knowledge states?▼

To plan adaptive classroom sessions from aggregate student knowledge states, provide a knowledge graph file in graphs/*.json and a session duration. The system analyzes class-wide competence profiles to generate targeted instructional plans, groupings, and peer tutoring pairings.

How do I generate peer tutoring pairings based on student competence profiles?▼

You can generate peer tutoring pairings by reading assessed student states from graphs/*.json files. The system analyzes competence profiles and mastery gaps to suggest optimal peer tutoring pairings following UDL 3.0 guidelines for differentiated sessions.

What data format is required to plan adaptive instruction from class-wide data?▼

Planning adaptive instruction requires assessed student states formatted as JSON files stored in a graphs/*.json directory. These knowledge graph files provide the aggregate student states and competence profiles needed for kst_utils.py analytics computations.

Can I use Knowledge Space Theory analytics to optimize a 60-minute lecture plan?▼

Yes, you can optimize a 60-minute lecture plan by providing a knowledge graph file and a 60-minute session duration. The analytics identify items with high target scores and appropriate prerequisites to structure the lecture time effectively.

What do I need to compute class-wide knowledge states for differentiated instruction?▼

You need assessed student states saved as JSON files in the graphs/*.json directory and the scripts/kst_utils.py analytics module. These components compute class-wide knowledge states to determine targets, groupings, and peer tutoring pairings for differentiated sessions.