Overview

UMCF (UnaMentis Curriculum Format) is a JSON-based specification for educational content optimized for voice-based AI tutoring, built from the ground up for natural conversation.

Key Characteristics

  • Voice-Native: Every text field can have TTS-optimized spoken variants
  • Tutoring-First: Built for natural conversation, not LMS delivery
  • Unlimited Hierarchy: Topics can nest to arbitrary depth
  • Standards-Grounded: Maps to IEEE LOM, LRMI, SCORM, xAPI, QTI, CASE, Open Badges
  • AI-Enrichment Ready: Designed for automated content enhancement

Here's an example of the basic UMCF document structure in JSON:

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

What UMCF Provides

UMCF is designed specifically for the unique needs of voice-based AI tutoring:

Capability UMCF Feature
Pronunciation guides Built-in spokenText variants
Comprehension checks Inline stopping points
Alternative explanations First-class support
Misconception handling Remediation triggers
Conversation flow Natural dialogue structure
Depth adaptation Multiple depth levels

Document Structure

A UMCF document has a hierarchical structure:

Curriculum Root container with metadata
Metadata Title, version, language, audience
Topics[] Learning content hierarchy
Glossary Terms with pronunciations
Topic Single learning unit
Learning Objectives
Transcript
Stopping Points
Alternatives
Misconceptions
Assessments
Examples
Children[]

Voice-Native Features

UMCF 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

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

Overview 2-5 min

High-level intuition without technical detail. Perfect for quick introductions.

Introductory 5-15 min

Basic concepts with minimal mathematics. Suitable for beginners.

Intermediate 15-30 min

Moderate detail with equations mentioned but not derived.

Advanced 30-60 min

In-depth coverage with derivations and proofs.

Graduate 60-120 min

Comprehensive treatment with full mathematical rigor.

Research 90-180 min

Paper-level depth with citations and cutting-edge content.

Topic Structure

Each topic contains the content and metadata for a single learning unit. Here's an example topic definition in JSON:

{
  "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

UMCF allows content authors to anticipate common misunderstandings:

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

Assessments

UMCF supports various assessment types optimized for voice interaction:

Reflection Questions

Open-ended prompts for deeper thinking

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

Verbal Quizzes

Quick knowledge checks with spoken answers

{
  "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..."
  }
}

Application Problems

Real-world problem solving scenarios

{
  "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?"
  ]
}

Learning Science Features

UMCF includes pedagogical features grounded in learning science research to promote genuine understanding and long-term retention.

Teachback Checkpoints

Students explain concepts in their own words. The AI evaluates depth and accuracy with tiered feedback.

{
  "type": "teachback",
  "prompt": "In your own words, explain
  what a derivative represents.",
  "tiers": {
    "excellent": "≥0.9 accuracy",
    "good": "≥0.7 accuracy",
    "partial": "≥0.4 accuracy",
    "struggling": "<0.4 accuracy"
  }
}

Spaced Retrieval

Key concepts are flagged for periodic review using proven spacing algorithms to strengthen memory.

{
  "retrieval": {
    "algorithm": "sm2",
    "intervals": [1, 3, 7, 14, 30],
    "conceptId": "derivative-definition",
    "prompt": "What does a derivative
    measure?"
  }
}

Productive Struggle Metrics

Tracks and celebrates cognitive effort. Think time before responding is measured and encouraged.

{
  "metrics": {
    "thinkTimeSeconds": 45,
    "teachbackAttempts": 2,
    "encouragement": "You invested
    45 seconds of deep thinking.
    That's how real learning happens."
  }
}

Import System

UMCF includes a pluggable import system for converting existing content:

MIT OpenCourseWare

Import courses from MIT OpenCourseWare

OCW → UMCF Complete

CK-12 FlexBooks

Import K-12 EPUB textbooks from CK-12 Foundation

EPUB → UMCF Complete

EngageNY

Import K-12 curriculum from EngageNY

EngageNY → UMCF Complete

MERLOT

Import learning materials from MERLOT repository

MERLOT → UMCF Complete

Fast.ai Notebooks

Import Jupyter notebooks from fast.ai courses

Jupyter → UMCF Spec Complete

Stanford SEE

Import courses from Stanford Engineering Everywhere

SEE → UMCF Spec Complete

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

AI Curriculum Generation

In addition to importing existing content, UnaMentis provides a comprehensive prompt system for generating UMCF-compliant curriculum from scratch using AI models like Claude or GPT-4.

Generation Prompt Features

  • Complete Format Compliance: Generates valid UMCF v1.1.0 JSON
  • Voice-Optimized Content: Speaking notes, pronunciations, and TTS-friendly text
  • Teachback Checkpoints: Comprehension verification built in
  • Bloom's Taxonomy Alignment: Learning objectives at appropriate cognitive levels
  • Assessment Generation: Quizzes with feedback included
  • Misconception Handling: Common errors with remediation strategies

How to Use

1

Copy the Prompt

Get the generation prompt from the documentation.

2

Add Your Specification

Specify the topic, target audience, scope, and desired depth level.

3

Generate with AI

Submit to Claude, GPT-4, or another capable model.

4

Review and Save

Validate the output JSON and save with a .umcf extension.

This approach lets you create custom curriculum for any topic, tailored to your specific audience and learning goals, without requiring technical knowledge of the UMCF format.

Standards Mapping

UMCF fields map to established educational technology standards for interoperability:

Standard Purpose UMCF Mapping
IEEE LOM Metadata curriculum.metadata.*
LRMI Learning resources topic.learningObjectives, topic.depthLevel
SCORM Content packaging curriculum structure, sequencing
xAPI Learning analytics assessment events, progress tracking
QTI Assessments topic.assessments.*
CASE Competency frameworks learningObjectives, masteryThresholds
Open Badges Credentials curriculum.badges, topic.achievements