We collaborated with our sponsor, NAU's MRTL, to define the initial scope of the TutorTech platform, drafting requirements and identifying the core functionality needed for a successful AI-assisted tutoring system.
Our team designed the overall architecture, including the split between the React frontend, Flask backend, PostgreSQL database, and Qdrant for vector search integration. Major design diagrams and wireframes were completed in this phase.
Development of the Flask backend API, database schema design, user authentication, chat routes, learning preferences, and session persistence was completed. Hosted backend on Render.
Built the React web application for user interaction, course enrollment, learning style customization, and dynamic AI chat functionality. Frontend was deployed on Vercel for scalable hosting.
Conducted iterative testing on user signup, login, chat interactions, course progression, and preferences settings. Improved usability based on test feedback and fixed backend CORS and session persistence bugs.
Integrated all modules, finalized the demo version, and completed sponsor deliverables. Conducted a full walkthrough with a live demonstration hosted at tutor-tech.vercel.app.
August - December: Early development stages focused on setting up the foundation, including UI prototyping, database design, backend API development, AI integration, and user access systems.
January - May: Focus shifted to refining the platform through UI enhancements, expanding the database, conducting usability testing, finalizing features and documentation, and implementing advanced AI capabilities.
