This prototype prioritizes system architecture, AI logic, and user workflows over full production wiring.
This is a proof-of-concept LMS prototype designed specifically for K-12 education,featuring advanced AI-powered capabilities to enhance both teaching and learning experiences. The system demonstrates intuitive design, robust functionality, and innovative use of artificial intelligence.
- Course Management: Create and manage multiple courses across different grade levels and subjects
- Assignment Creation: Design assignments with customizable difficulty levels and point values
- AI-Assisted Grading: Automated grading system that provides:
- Intelligent score calculation based on content quality
- Detailed feedback tailored to student performance
- Actionable suggestions for improvement
- Class Analytics: Visual performance tracking with:
- Grade distribution charts
- Average class performance metrics
- Individual student progress monitoring
- Course Dashboard: Clean, organized view of all enrolled courses
- Assignment Submission: Easy-to-use submission interface with status tracking
- Progress Tracking: Visual charts showing grade progression over time
- AI-Powered Recommendations: Personalized features including:
- Custom learning paths based on performance history
- Targeted resource recommendations (workshops, videos, enrichment)
- Intelligent study material suggestions matched to skill level
- Performance prediction for upcoming assignments
- Automated Grading: Analyzes submission content and provides scores with intelligent feedback
- Personalized Learning Paths: Identifies strengths/weaknesses and recommends tailored resources
- Intelligent Content Recommendations: Suggests study materials at appropriate difficulty levels
- Performance Prediction: Forecasts student performance on upcoming assignments based on historical data
- Adaptive Difficulty: Adjusts recommendations based on performance trends
- Python 3.7 or higher
- Jupyter Notebook or JupyterLab
- Install required packages:
pip install ipywidgets pandas matplotlib numpy jupyter- Enable ipywidgets for Jupyter:
jupyter nbextension enable --py widgetsnbextension- Open
prototype.ipynbin Jupyter Notebook/Lab - Run all cells in order (Cell → Run All)
- The interactive LMS interface will appear at the bottom
- Click any user button to login and explore features
- Ms. Johnson - Teaches Grade 7 Math & Science
- Mr. Davis - Teaches Grade 8 English & History
- Emma Wilson (Grade 7) - Strong in math, working on writing skills
- Liam Brown (Grade 7) - Science-oriented, needs math support
- Olivia Garcia (Grade 7) - High performer across all subjects
- Noah Martinez (Grade 8) - Focuses on history, English is strength
- Sophia Anderson (Grade 8) - Excellent all-around student
-
Introduction (30 seconds)
- Show the login screen
- Highlight the AI-powered features listed
-
Teacher Perspective (2-3 minutes)
- Login as Ms. Johnson
- Navigate through "My Courses" tab to show course overview
- Go to "Grade Assignments" and demonstrate AI grading
- Click "🤖 AI Grade" button to show automated grading in action
- Visit "Create Assignment" to show how easy it is to add new work
- Check "Analytics" tab for class performance visualizations
-
Student Perspective (2-3 minutes)
- Logout and login as Emma Wilson
- Show "My Courses" with assignment statuses
- Submit a new assignment to demonstrate the workflow
- Visit "Progress" tab to show grade progression charts
- Explore "AI Recommendations" to highlight personalized learning paths
- Check "Upcoming" tab to see AI performance predictions
-
Highlight AI Features (1 minute)
- Return to teacher view and grade the newly submitted assignment
- Show how AI provides instant feedback and suggestions
- Emphasize how this saves teacher time while providing quality feedback
-
Conclusion (30 seconds)
- Summarize the three pillars: Usability, Functionality, AI Innovation
- Mention scalability and real-world applicability
- Frontend: IPyWidgets for interactive UI components
- Data Management: Python classes with in-memory data structures
- Visualization: Matplotlib for charts and analytics
- AI Logic: Custom algorithms simulating ML-based recommendations
- Modular design with separate functions for each feature
- Clean separation between teacher and student interfaces
- Reusable AI assistant class for all intelligent features
- Responsive UI with real-time updates
- Educational AI: Not just generic automation, but education-specific intelligence
- Dual Interface: Seamlessly serves both teacher and student needs
- Real-time Feedback: Instant AI responses for improved learning loops
- Predictive Analytics: Forward-looking insights to prevent student struggles
- Personalization at Scale: Individual recommendations for every student
- Unique AI-powered features not commonly found in basic LMS platforms
- Personalized learning paths that adapt to individual student needs
- Visual analytics that make data accessible and actionable
- Intuitive tab-based navigation
- Clear visual hierarchy with color-coded status indicators
- One-click operations for common tasks
- Helpful tooltips and guidance throughout
- Clean, maintainable code with proper documentation
- Efficient data structures and algorithms
- Error-free operation with smooth state management
- Professional UI design with consistent styling
- Automated grading saves teacher time while maintaining quality
- Personalized recommendations improve student outcomes
- Performance prediction enables proactive intervention
- Content matching ensures appropriate challenge levels
If given more development time, potential additions include:
- Real database integration (PostgreSQL/MongoDB)
- User authentication with encryption
- Parent portal for progress monitoring
- Discussion forums and collaboration tools
- Mobile app companion
- Integration with external learning resources (Khan Academy, etc.)
- Advanced ML models for even better predictions
- Accessibility features (screen reader support, multilingual)
This LMS prototype demonstrates how AI can:
- Reduce Teacher Workload: Automated grading can save hours per week
- Improve Student Outcomes: Personalized paths address individual needs
- Enable Data-Driven Decisions: Analytics highlight intervention opportunities
- Scale Quality Education: AI assistance brings expert-level guidance to every student
Prototype Submitted By: [Your Name]
Date: December 2025
For: Dynamic Active, Inc. - Software Developer Internship
Thank you to Dr. Ashley Kauffman and the Dynamic Active team for this opportunity to showcase technical skills while contributing to educational innovation. I'm excited about the possibility of joining your team and making a real impact on K-12 education!
Note: This prototype is built with Python and Jupyter for rapid development and demonstration. A production version would utilize modern web frameworks (React/Vue.js frontend, Node.js/Django backend) with cloud deployment for scalability.