# Project export: Triad - Learn Your Way This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small. ## Project metadata - Hackathon: UC Berkeley AI Hackathon 2026 - Tagline: Triad transforms teacher-uploaded materials into personalized audio, visual, and text lessons. The platform adapts based on student performance and provides teachers with actionable learning analytics - Devpost: https://devpost.com/software/triad-learn-your-way - GitHub: not linked - Video: https://www.youtube.com/embed/Sjaoyjo4xsk?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent - Team: contributor stats unavailable ## Devpost submission (written by the team) ### Overview Every classroom is filled with students who learn differently, yet educational content is still overwhelmingly delivered in a single format. Students with ADHD, dyslexia, auditory processing disorders, visual impairments, or simply different learning preferences are often expected to adapt to the material instead of the material adapting to them. Most existing platforms ask students how they prefer to learn. We believed there was a better approach. Triad determines a student's optimal learning modality through a short diagnostic assessment, then automatically delivers lessons in the format where they perform best—text, audio, or visual. Rather than relying on self-reported preferences, the platform continuously measures learning outcomes and adapts instruction accordingly. Triad is an AI-powered adaptive learning platform for both teachers and students. For Teachers Upload existing course materials (PDFs, documents, presentations) Automatically generate: concise lesson summaries practice quizzes audio narration visual learning content Automatically generate: concise lesson summaries practice quizzes audio narration visual learning content Monitor class-wide and individual student performance through an analytics dashboard Monitor class-wide and individual student performance through an analytics dashboard View each student's recommended learning modality and assessment history View each student's recommended learning modality and assessment history For Students Students first complete a diagnostic assessment that evaluates learning performance across multiple content modalities. Based on their results, Triad automatically presents future lessons in the format where they demonstrate the highest comprehension: 📖 Text-first learning 🎧 Audio narration 🎨 Visual explanations Students can also interact with an AI tutor through voice or text, complete quizzes, and track their progress over time. The platform also supports predefined accessibility accommodations that can bypass the diagnostic process when required (for example, mandatory audio narration or caption requirements). Our architecture consists of a React frontend backed by a Next.js API layer. Frontend React Vite Backend Next.js 16 App Router API Routes Infrastructure Supabase PostgreSQL database File storage Supabase PostgreSQL database File storage Upstash Redis Caching Student state management Upstash Redis Caching Student state management AI Stack Anthropic Claude (Opus 4.8) Generates multimodal educational content Anthropic Claude (Opus 4.8) Generates multimodal educational content OpenAI GPT-4o mini Powers the conversational AI tutor OpenAI GPT-4o mini Powers the conversational AI tutor Deepgram Text-to-Speech Speech-to-Text Deepgram Text-to-Speech Speech-to-Text Teachers upload instructional material, which is processed through our AI content pipeline to generate multimodal learning resources. Student assessment results are stored in Supabase, cached with Redis for fast retrieval, and used to personalize future lessons automatically. One of our biggest challenges was designing a system that actually measures how students learn best instead of simply asking for their preference. We also had to coordinate multiple AI services while maintaining a smooth user experience. This involved generating multimodal content, supporting conversational tutoring, and integrating real-time speech capabilities without making the platform feel fragmented. Another challenge was designing for accessibility from the beginning. We wanted accommodations to be treated as first-class features rather than afterthoughts, allowing students with predefined accessibility needs to receive appropriate content immediately. Built an end-to-end adaptive learning platform in a hackathon timeframe. Created an objective diagnostic assessment that recommends learning modalities based on demonstrated performance. Successfully integrated multiple AI providers into a single cohesive workflow. Designed separate teacher and student experiences with personalized dashboards. Implemented voice interaction, multimodal lesson generation, AI tutoring, and learning analytics in one unified platform. Building Triad reinforced that personalization is more than simply adding AI—it requires thoughtful measurement, feedback loops, and accessibility-first design. We also learned how powerful modern AI models become when each specializes in a specific role. Using Claude for educational content generation, GPT for conversational tutoring, and Deepgram for speech allowed us to leverage each model's strengths instead of forcing one model to do everything. Finally, we learned the importance of designing educational technology around measurable learning outcomes rather than assumptions about how students think they learn. Our next goal is to make Triad adaptive over time instead of relying solely on an initial assessment. Future improvements include: Continuous modality adjustment as students learn Personalized study plans generated from historical performance Teacher recommendations for intervention when students begin struggling LMS integrations (Canvas, Google Classroom, Blackboard) More accessibility options for diverse learning needs Longitudinal analytics showing how learning preferences evolve over an academic year Ultimately, we envision Triad becoming an AI-powered learning companion that continuously adapts alongside every student, ensuring educational content is delivered in the way they learn best—not the way it was originally created. ## README (from the GitHub repository) No README available. ## Detected evidence (automated analysis) No repository was indexed for this project. Claimed technologies below could not be checked against code. - JavaScript (language) — claimed on Devpost, not found in the code - Next.js (technology) — claimed on Devpost, not found in the code - React (technology) — claimed on Devpost, not found in the code - TypeScript (language) — claimed on Devpost, not found in the code ## Codebase structure No repository index available. ## Key source files No repository index available; no source files included.