UMLCF Curriculum Format

Overview

UMLCF (UnaMentis Markup Language Curriculum Format) is a JSON-based specification for educational content optimized for voice-based AI tutoring. Unlike traditional e-learning standards like SCORM or xAPI, UMLCF is built from the ground up for natural conversation.

{
  "umlcf_version": "1.0.0",
  "curriculum": {
    "id": "intro-calculus",
    "title": "Introduction to Calculus",
    "description": "A comprehensive introduction to differential calculus",
    "language": "en-US",
    "topics": [...]
  }
}

Why UMLCF?

Existing e-learning standards were designed for click-through courses and learning management systems. They don't address the unique needs of voice-based AI tutoring:

Document Structure

A UMLCF document has a hierarchical structure:

Voice-Native Features

UMLCF includes several features specifically designed for text-to-speech synthesis and natural voice interaction.

Spoken Text Variants

Every text field can have a spokenText variant optimized for TTS:

{
  "text": "The derivative of x² is 2x.",
  "spokenText": "The derivative of x squared is 2 x."
}

Pronunciation Guides

The glossary supports pronunciation hints for technical terms:

{
  "glossary": [{
    "term": "Euler",
    "definition": "Swiss mathematician (1707-1783)",
    "pronunciation": "OY-ler",
    "ipa": "/ˈɔɪlər/"
  }]
}

Stopping Points

Inline comprehension checks that pause the AI to verify understanding:

{
  "transcript": {
    "paragraphs": [
      "A derivative measures the rate of change of a function.",
      "Think of it as the slope of the function at any point."
    ],
    "stoppingPoints": [{
      "afterParagraph": 1,
      "type": "comprehensionCheck",
      "prompt": "Can you think of a real-world example where we care about rate of change?"
    }]
  }
}

Content Depth Levels

UMLCF supports multiple depth levels for the same content, allowing the AI tutor to adapt to the learner's needs:

Topic Structure

Each topic contains the content and metadata for a single learning unit:

{
  "id": "derivatives-introduction",
  "title": "Introduction to Derivatives",
  "depthLevel": "intermediate",
  "estimatedDuration": "PT20M",

  "learningObjectives": [
    "Define the derivative as a rate of change",
    "Calculate derivatives of polynomial functions",
    "Interpret the derivative geometrically as slope"
  ],

  "transcript": {
    "text": "Today we're going to explore derivatives...",
    "spokenText": "Today we're going to explore derivatives.",
    "stoppingPoints": [...]
  },

  "alternativeExplanations": [{
    "id": "simple-analogy",
    "style": "analogy",
    "content": "Think of it like a speedometer in your car..."
  }, {
    "id": "technical",
    "style": "technical",
    "content": "Formally, the derivative is defined as the limit..."
  }],

  "misconceptions": [{
    "id": "derivative-is-tangent",
    "description": "Confusing derivative with tangent line",
    "triggers": ["the derivative is a line", "derivative touches the curve"],
    "remediation": "The derivative gives us the slope of the tangent line..."
  }],

  "children": [
    { "id": "power-rule", ... },
    { "id": "chain-rule", ... }
  ]
}

Alternative Explanations

When a learner doesn't understand, the tutor can try a different approach:

• simple: Plain language, no jargon
• technical: Formal definitions and notation
• analogy: Real-world comparisons
• visual: Descriptions of diagrams or animations
• example: Worked examples

Misconception Handling

UMLCF allows content authors to anticipate common misunderstandings:

• Triggers: Phrases that indicate the misconception
• Remediation: Corrective explanation
• Prerequisites: Topics to review if misconception persists

Assessments

UMLCF supports various assessment types optimized for voice interaction:

{
  "type": "reflection",
  "prompt": "How might derivatives help
  in predicting stock prices?",
  "rubric": [
    "Mentions rate of change",
    "Connects to real-world prediction"
  ]
}

{
  "type": "verbalQuiz",
  "question": "What is the derivative
  of x cubed?",
  "expectedPatterns": [
    "3 x squared",
    "three x to the second"
  ],
  "feedback": {
    "correct": "Exactly right!",
    "incorrect": "Remember the power rule..."
  }
}

{
  "type": "application",
  "scenario": "A ball is thrown upward...",
  "questions": [
    "When is the ball at its highest?",
    "What is the maximum height?"
  ],
  "hints": [
    "What happens to velocity at the peak?"
  ]
}

Import System

UMLCF includes a pluggable import system for converting existing content:

AI Enrichment Pipeline

Raw imported content can be enhanced using a 7-stage LLM pipeline:

1. Content Analysis: Identify structure and key concepts
2. Segmentation: Break into conversational chunks
3. Objective Extraction: Generate learning objectives
4. Assessment Generation: Create quizzes and reflections
5. Tutoring Enhancement: Add stopping points and misconceptions
6. Alternative Explanations: Generate multiple explanation styles
7. Knowledge Graph: Build concept relationships

Standards Mapping

UMLCF fields map to established educational technology standards for interoperability: