ai-adaptive-learning-master

What problem does it solve?

This Skill helps practitioners design, evaluate, and operationalize AI Adaptive Learning systems using validated mental models, evidence standards, and production workflows instead of vague “personalization” claims.

Core Features & Use Cases

• Agentic Protocol for research-first answers: structures investigation across student modeling, KC granularity, evidence auditing, compliance mapping, tool ecosystem fit, learning tradition, and LLM impact before responding.
• Master OS decision playbooks: checks whether a system is truly adaptive, selects knowledge tracing approaches by data scale, and enforces assessment rigor (IRT calibration for CAT).
• End-to-end implementation walkthroughs: supports key pipelines like knowledge component analysis, item authoring & psychometric calibration, student model training/validation, adaptive delivery engine design, spaced repetition scheduling, learning analytics dashboards, RCT/effect evaluation, LMS integration (LTI/xAPI), compliance & privacy pipeline, and LLM tutoring deployment.

Quick Start

Tell your AI agent you are building an AI Adaptive Learning tutoring workflow for the topic “knowledge tracing” and ask it to run the Agentic Protocol using the Skill’s research dimensions, then output an implementation plan and an evidence/compliance checklist.