Software Specification: Modern Money Lab Learning Management System (MML-LMS)

Executive Summary

The Modern Money Lab Learning Management System (MML-LMS) is a comprehensive educational platform specifically designed for Modern Monetary Theory instruction. It addresses the unique challenges of delivering MMT education globally with distributed teaching staff and varied student needs. The platform combines traditional LMS functionality with innovative features like AI-powered assessment, flexible learning paths, and white-label partner portals. By automating preliminary assessments, enabling granular progress tracking, and facilitating ongoing relationships with alumni, MML-LMS reduces teaching bottlenecks while creating new revenue streams through taster courses and consulting partnerships. This tool positions itself as the central hub for MMT education delivery, potentially serving as a model for other specialized economic education platforms within the broader MMT ecosystem.

Tool Overview

Type: web application with API

Description: A specialized learning management system for delivering Modern Monetary Theory courses with flexible sequencing, automated assessment tools, instructor collaboration features, revenue management, and white-label partnership capabilities.

Primary Users: MMT students (degree and taster course participants), distributed teaching staff, MML administrators, partner organizations, and alumni

Problem Solved: Eliminates teaching bottlenecks caused by limited staff resources and remote coordination while enabling scalable MMT education delivery with multiple revenue streams

Key Features

[MVP] Flexible Learning Path Engine

Allows students to choose custom sequences through 13 subjects while enforcing prerequisites and tracking recommended vs. actual paths

User Story: As a student, I want to study subjects in my preferred order while meeting prerequisites so that I can learn at my own pace and focus on my interests

Complexity: medium

[MVP] Granular Progress Tracking Dashboard

Tracks completion at lecture, module, and subject levels with visual progress indicators and time-spent analytics

User Story: As an instructor, I want to see exactly where each student is in their learning journey so that I can provide targeted support

Complexity: simple

[MVP] Multi-Modal Assessment System

Supports auto-graded quizzes, AI-powered essay feedback, and peer review workflows with preliminary scoring to assist instructors

User Story: As a teaching assistant, I want preliminary assessment scores and feedback generated automatically so that I can focus on high-value interventions

Complexity: complex

[POST-MVP] Adaptive Learning Recommendations

Suggests next steps, supplementary materials, and remedial content based on performance analytics and learning patterns

User Story: As a student, I want personalized recommendations for what to study next so that I can optimize my learning outcomes

Complexity: complex

[MVP] Global Instructor Collaboration Hub

Provides shared assignment grading interfaces, student communication dashboards, and content management areas for distributed faculty

User Story: As a remote instructor, I want to collaborate seamlessly with other faculty members so that we can provide consistent student support

Complexity: medium

[MVP] Integrated Discussion Forums

Subject-specific and cross-curricular discussion spaces with instructor moderation tools and student peer interaction features

User Story: As a student, I want to discuss MMT concepts with peers and instructors so that I can deepen my understanding through dialogue

Complexity: simple

[MVP] Revenue Management System

Handles enrollment payments, subscription billing, taster course purchases, and partner revenue sharing with integrated payment processing

User Story: As an administrator, I want to manage all course payments and subscriptions in one place so that I can track revenue and manage access

Complexity: medium

[POST-MVP] Taster Course Builder

Creates shortened courses from existing content with separate pricing, access controls, and conversion tracking to full programs

User Story: As an administrator, I want to quickly create preview courses from existing content so that I can attract new students and generate additional revenue

Complexity: medium

[FUTURE] White-Label Partner Portal

Allows partner organizations to brand and customize course delivery with their own logos, colors, and domain while using MML content

User Story: As a partner organization, I want to offer MMT courses under my own brand so that I can serve my audience while leveraging MML expertise

Complexity: complex

[FUTURE] Alumni & Continuing Education Portal

Provides ongoing access to refresher content, new course modules, and consultation booking systems for graduates

User Story: As an alumni, I want to stay connected with MML and access updated content so that I can maintain my expertise and grow my consulting practice

Complexity: medium

[POST-MVP] Content Version Control System

Manages updates to the 240+ hours of lecture content with versioning, approval workflows, and automatic student notification of changes

User Story: As an instructor, I want to update course content without disrupting active students so that I can keep materials current while maintaining consistency

Complexity: medium

[MVP] Assignment Submission & Feedback Pipeline

Handles document uploads, plagiarism checking, AI preliminary review, instructor final review, and grade distribution with feedback tracking

User Story: As a student, I want to submit assignments and receive comprehensive feedback efficiently so that I can improve my understanding and writing

Complexity: medium

User Workflows

Student Course Enrollment & Learning Flow

Steps:

1. Student browses available courses and taster sessions
2. System processes payment and grants access to selected content
3. Student views personalized dashboard with recommended learning path
4. Student watches lectures, takes quizzes, and participates in discussions
5. System tracks progress and provides completion certificates

Screens: Course Catalog, Payment Portal, Student Dashboard, Video Player, Assessment Interface, Discussion Forums, Progress Tracking

Instructor Assignment Review Process

Steps:

1. Student submits assignment through upload interface
2. System runs plagiarism check and AI preliminary assessment
3. Instructor receives notification with preliminary scores and flagged items
4. Instructor reviews, modifies scores, and adds detailed feedback
5. System notifies student and updates grade in progress tracking

Screens: Assignment Submission, Instructor Dashboard, Grading Interface, Feedback Editor, Grade Management

Partner Organization White-Label Setup

Steps:

1. Partner completes partnership application and payment setup
2. Administrator configures partner portal with custom branding
3. Partner selects courses and customizes content descriptions
4. System generates partner-specific enrollment links and payment processing
5. Partner launches courses to their audience with MML content

Screens: Partner Application, Branding Configuration, Content Selection, Payment Setup, Partner Dashboard

Data Requirements

Student

Fields: user_id, enrollment_status, payment_history, learning_path_preferences, progress_data, assessment_scores, discussion_participation

Relationships: Many-to-many with Courses, one-to-many with Assignments, one-to-many with Progress Records

Storage Notes: GDPR compliance required for international students, encrypted payment data

Course

Fields: course_id, subject_area, lecture_videos, prerequisites, estimated_hours, pricing_tiers, assessment_criteria

Relationships: Many-to-many with Students, one-to-many with Assignments, many-to-many with Instructors

Storage Notes: Large video files require CDN storage, version control for content updates

Assessment

Fields: assessment_id, type, questions, rubrics, ai_feedback_templates, peer_review_settings, automatic_scoring_rules

Relationships: Belongs to Course, has many Student Responses, assigned to Instructors

Storage Notes: Support for multiple question types and file uploads

Instructor

Fields: instructor_id, specialization_areas, availability_schedule, grading_preferences, collaboration_permissions, time_zone

Relationships: Many-to-many with Courses, one-to-many with Assessment Reviews, many-to-many with Students

Storage Notes: Global distribution requires time zone handling

Partner Organization

Fields: partner_id, branding_assets, custom_domain, revenue_share_rate, content_permissions, student_data_access_level

Relationships: Has many Partner Students, has access to subset of Courses

Storage Notes: Separate tenant data isolation for white-label partners

Integrations with MMT Ecosystem

No specific existing MMT projects identified in knowledge base

Type: API endpoints for future integration

MML-LMS should expose student progress and alumni data via API for potential integration with MMT advocacy platforms and job boards

Data Exchanged: Student credentials, course completion certificates, specialized skills data

Technical Considerations

Suggested Stack: React/Next.js frontend, Node.js/Express backend, PostgreSQL database, Redis for caching, AWS S3 for video storage with CloudFront CDN, Stripe for payment processing, Socket.io for real-time discussions

Hosting: AWS or similar cloud platform with global CDN for video content delivery to international students

Authentication: JWT-based authentication with role-based access control (student/instructor/admin/partner), OAuth integration for social login

Key Challenges:

• Video streaming optimization for global audience
• AI integration for essay feedback
• Multi-tenant architecture for white-label partners
• Complex progress tracking across flexible learning paths

MVP Scope

Included in MVP:

• Flexible Learning Paths
• Progress Tracking
• Basic Assessment System
• Instructor Collaboration Tools
• Discussion Forums
• Payment Processing
• Assignment Submission Pipeline

Excluded from MVP (Future):

• AI-powered feedback
• White-label portals
• Alumni systems
• Advanced analytics
• Mobile apps

Success Criteria: Students can enroll, complete courses in custom sequences, submit assignments, and instructors can grade efficiently with 50% less time spent on administrative tasks

Development Phases

Phase 1: Core Educational CMS

Deliverables:

• User authentication system
• Video streaming platform
• Progress tracking database
• Basic assessment tools
• Instructor grading interface
• Payment processing integration

Phase 2: Advanced Learning & Revenue Features

Deliverables:

• AI assessment integration
• Adaptive learning engine
• Taster course builder
• Content versioning system
• Advanced analytics dashboard

Phase 3: Partnership & Alumni Platform

Deliverables:

• Multi-tenant white-label system
• Alumni portal
• Consultation booking system
• Partner revenue sharing automation
• Mobile responsive optimization

Next Steps

1. Create detailed database schema with emphasis on flexible learning path tracking
2. Set up development environment with video streaming capabilities
3. Design API endpoints for instructor collaboration and student progress management
4. Research and prototype AI integration options for assessment automation
5. Create wireframes for core user interfaces (student dashboard, instructor grading, admin panel)