AURA Core

Agentic Unified Reinforcement for Academics

A multi-agent AI framework for designing, developing, and quality-assuring educational content at scale.

The Problem

Educational course development is fragmented across multiple specialties—curriculum design, content writing, presentation creation, assessment design, and quality assurance. This leads to:

• Misalignment between learning outcomes, activities, and assessments
• Inconsistent quality and accessibility standards
• Difficulty coordinating specialized expertise
• No systematic quality gates before delivery

The Solution

AURA provides an orchestrated multi-agent system where 7 specialized AI agents work in sequence, each with defined responsibilities, built-in quality gates, and evidence-based pedagogical frameworks.

┌─────────────────────────────────────────────────────────────────┐
│                    AURA Execution Flow                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   Orchestrator ──► Content Developer ──► Presentation Creator   │
│        │                                        │               │
│        │                                        ▼               │
│        │              ◄── QA Educator ◄── Assessment Architect  │
│        │                      │                                 │
│        │                      ▼                                 │
│        └──────────────► Quality Gate ──► Ready for Delivery     │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Installation

# Run directly in your project
npx aura-edu-core

# Or install globally
npm install -g aura-edu-core

This copies the .aura-core/ framework and installs Claude Code slash commands.

The 7 Specialized Agents

| Agent | Role | Focus | |-------|------|-------| | Educational Orchestrator | Coordinates end-to-end workflows | Progress tracking, stage transitions | | Educational Designer | Structures courses and outcomes | Bloom's alignment, scaffolding | | Content Developer | Drafts lessons, examples, exercises | Practice-first, worked examples | | Presentation Creator | Creates slides and visuals | Dual-coding, accessibility | | Assessment Architect | Designs tests and rubrics | Validity, reliability, Bloom levels | | Learning Curve Specialist | Optimizes difficulty progression | Pacing, spacing, scaffolding | | QA Educator | Quality assurance gate | O→A→T traceability, accessibility |

Core Workflows

1. Course Planning

Research → Outcomes Map → Curve Analysis → Assessment Strategy → QA Gate

2. Course Execution (per lesson)

Shard Course → Draft Lesson → Create Slides → Build Assessment → QA Review

Artifact Standard Paths

All outputs follow consistent file paths:

docs/courses/{course_slug}.md              # Course specification
docs/outcomes/{course_slug}-map.md         # O→A→T matrix
docs/lessons/{course_slug}/{lesson_id}.md  # Lesson scripts
docs/slides/{course_slug}/{lesson_id}.md   # Presentations
docs/assessments/{course_slug}/{id}.md     # Assessments
docs/rubrics/{course_slug}/{id}.md         # Rubrics
docs/qa/gates/{course_slug}-{id}.yml       # QA decisions

Project Structure

.aura-core/
├── agents/          # 7 agent role definitions
├── workflows/       # Planning & execution orchestration
├── templates/       # YAML/MD blueprints for artifacts
├── checklists/      # Quality & accessibility criteria
├── tasks/           # Executable task guides
├── data/            # Pedagogical frameworks & knowledge base
└── docs/            # User guides & documentation

Embedded Pedagogical Frameworks

• Bloom's Taxonomy — 6 cognitive levels with measurable verbs
• Assessment Levels — L1 (micro-formative) to L4 (capstone)
• Instructional Patterns — Worked examples, retrieval practice, dual coding
• Cognitive Load Theory — Chunking, signaling, modality principles
• Accessibility Standards — WCAG AA equivalent baseline
• O→A→T Traceability — Outcomes → Activities → Tests alignment

Quality Gates

Every lesson passes through a QA gate before delivery:

| Status | Meaning | |--------|---------| | PASS | Ready for delivery | | CONCERNS | Minor issues noted, can proceed | | FAIL | Must address issues before delivery | | WAIVED | Exception granted with justification |

Claude Code Slash Commands

After installation, use these commands in Claude Code:

| Command | Agent | Description | |---------|-------|-------------| | /orchestrator | Educational Orchestrator | Start workflows, shard courses | | /designer | Educational Designer | Design courses, create outcome maps | | /content | Content Developer | Draft lessons, examples, exercises | | /slides | Presentation Creator | Create slide decks and visuals | | /assessment | Assessment Architect | Design tests and rubrics | | /curve | Learning Curve Specialist | Analyze difficulty progression | | /qa | QA Educator | Quality review, gate decisions |

Example Usage

/orchestrator start the python-basics course workflow
/content develop lesson L01 on variables
/slides create presentation for L01
/assessment design quiz for L01
/qa review L01 and issue gate decision

Key Principles

1. Outcomes First — Everything traces back to learning outcomes
2. Agent Specialization — Each agent has a focused responsibility
3. Progressive Scaffolding — Content builds systematically
4. Feedback Loops — Continuous quality checks
5. Accessibility by Default — Inclusive design from the start

Requirements

• Node.js 18+
• npm or npx

License

MIT

Built for educators who want AI assistance without sacrificing pedagogical quality.