adaptivetest

Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "adaptivetest" with this command: npx skills add woodstocksoftware/adaptivetest-skill

AdaptiveTest

Production-grade adaptive testing API. Uses Item Response Theory (IRT 2PL/3PL) with Computerized Adaptive Testing (CAT) to deliver precise ability estimates in fewer questions. Includes AI-powered question generation and personalized learning recommendations.

When to Use This Skill

Use AdaptiveTest when the user needs to:

  • Create or manage assessments and tests
  • Run adaptive testing sessions that select questions based on student ability
  • Generate assessment questions by topic, difficulty, or academic standard
  • Get personalized learning recommendations for students
  • Calibrate test items using IRT parameter estimation
  • Manage students, classes, and enrollments
  • Analyze test results and track student mastery

Authentication

All requests require the X-API-Key header:

X-API-Key: ${ADAPTIVETEST_API_KEY}

Base URL: https://adaptivetest-platform-production.up.railway.app/api

Core Workflows

1. Create and Administer an Adaptive Test

POST /tests              -- Create a test (set cat_enabled: true)
POST /tests/{id}/items   -- Add items to the test
POST /tests/{id}/sessions -- Start an adaptive session for a student
GET  /sessions/{id}/next-item -- Get the next CAT-selected item
POST /sessions/{id}/responses -- Submit student response
GET  /sessions/{id}/results   -- Get ability estimate and results

The CAT engine selects items using maximum Fisher information. Ability is estimated after each response using IRT 2PL or 3PL models. Sessions terminate when the standard error drops below threshold or max items are reached.

2. Generate Questions with AI

POST /gen-q -- Generate questions by topic, difficulty, and standard

Request body:

{
  "topic": "Quadratic equations",
  "difficulty": "medium",
  "count": 5,
  "standard": "CCSS.MATH.CONTENT.HSA.REI.B.4",
  "format": "multiple_choice"
}

Returns QTI 3.0-compatible items with stems, distractors, and rationales. Generation takes ~7 seconds.

3. Get Learning Recommendations

POST /recs -- Get personalized learning recommendations for a student

Request body:

{
  "student_id": "student-uuid",
  "subject": "Mathematics",
  "include_resources": true
}

Returns a personalized learning plan based on the student's ability profile and assessment history. Generation takes ~25 seconds.

4. Calibrate Test Items

POST /tests/{id}/calibrate -- Run IRT calibration on collected response data

Requires sufficient response data (minimum 30 responses per item recommended). Returns IRT parameters: difficulty (b), discrimination (a), and guessing (c) for 3PL.

5. Manage Students and Classes

POST /students           -- Create a student
GET  /students           -- List students
POST /classes            -- Create a class
POST /classes/{id}/enroll -- Enroll students in a class

OneRoster 1.2 compatible for SIS integration.

6. View Results and Analytics

GET /sessions/{id}/results       -- Detailed session results with ability estimate
GET /students/{id}/history       -- Assessment history for a student
GET /tests/{id}/analytics        -- Item-level analytics for a test

Rate Limits

Rate limits depend on your API key tier. Check X-RateLimit-Remaining header on each response.

Error Handling

All errors return JSON with a detail field:

{"detail": "Human-readable error message"}

Common status codes: 400 (validation), 401 (auth), 403 (limit exceeded), 404 (not found), 429 (rate limited).

Reference Documentation

For detailed endpoint specifications, request/response shapes, and IRT/CAT concepts, see the references/ directory:

  • references/api-endpoints.md -- Full endpoint reference
  • references/adaptive-testing.md -- IRT and CAT concepts
  • references/calibration.md -- Item calibration guide

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

General

Ai Competitor Analyzer

提供AI驱动的竞争对手分析,支持批量自动处理,提升企业和专业团队分析效率与专业度。

Registry SourceRecently Updated
General

Ai Data Visualization

提供自动化AI分析与多格式批量处理,显著提升数据可视化效率,节省成本,适用企业和个人用户。

Registry SourceRecently Updated
General

Ai Cost Optimizer

提供基于预算和任务需求的AI模型成本优化方案,计算节省并指导OpenClaw配置与模型切换策略。

Registry SourceRecently Updated