Study-Mate: AI Powered Study Success Platform.

This is a submission for the Heroku "Back to School" AI Challenge

What I Built

StudyMate is an intelligent study management platform designed to help students track, optimize, and transform their study habits. Beyond traditional features like session tracking, group collaboration, and performance analytics, StudyMate AI introduces “MentorMind” – a built-in AI-powered study assistant that learns from each student’s unique learning patterns.

Features

• 🕒 Study Timer

  • Customizable Pomodoro-style study sessions
  • Session tracking and statistics

• 📈 Performance Analytics

  • Visual progress tracking
  • Monthly study hour analysis
  • Performance trends and insights

• 🤖 AI Study Assistant

  • Personalized study advice
  • Context-aware recommendations
  • Real-time study habit analysis
  • Interactive chat interface
  • Study technique suggestions based on performance

• 👥 Study Groups

  • Create and join study groups
  • Group chat functionality
  • Collaborative study sessions

This help students not only manage their study time effectively but also receive tailored advice to enhance their learning experience.

Category

primary: Student Success
secondary: Crazy Creative

Demo

Study-Mate

prime399 / study-mate

StudyMate AI 🎓

An intelligent study management platform with AI-powered personalized assistance

StudyMate AI helps students track, optimize, and transform their study habits through real-time analytics, collaborative features, and MentorMind - an AI assistant that learns from your unique learning patterns.

✨ Features

• 🕒 Smart Study Timer - Customizable Pomodoro sessions with progress tracking
• 🤖 MentorMind AI Assistant - Personalized study advice using RAG-like architecture
• 📈 Performance Analytics - Visual progress tracking and insights
• 👥 Study Groups - Collaborative spaces with real-time messaging
• ✅ Task Management - Drag-and-drop todo board with priorities
• 🏆 Leaderboards - Global and group-specific study rankings
• 📅 Calendar Integration - Study session scheduling and planning

🚀 Quick Start

Prerequisites

• Node.js 18+ and pnpm
• Convex account (convex.dev)
• Vercel account (for deployment)
• Heroku Inference API access for AI features

1. Clone & Install

git clone https://github.com/prime399/study-mate.git
cd study-mate
pnpm install

2. Environment Setup

Create .env.local for…

View on GitHub

• Dashboard:

• Study-Timer:

• MentorMind:

My project leverages Heroku's Managed Inference and Agents to create an intelligent, adaptive study companion:

1. Multi-Agent Architecture

I implemented a sophisticated three-agent system, each optimized for specific use cases:

• GPT-OSS Agent: Handles general study advice, learning strategies, and educational content generation. This agent provides comprehensive explanations and study methodology recommendations.

• Nova Lite Agent: Optimized for real-time interactions and quick responses. Powers the interactive chat interface where students need immediate feedback and guidance during study sessions.

• Claude Agent: Specializes in deep analysis of study patterns, performance metrics, and behavioral insights. Processes complex data to generate personalized long-term study plans and identifies learning bottlenecks.

2. Intelligent Agent Orchestration

The agents work in coordination through a custom routing system:

• User queries are analyzed and routed to the most appropriate agent based on complexity, urgency, and context
• Agents share context about the student's profile, including study history, performance trends, and learning preferences
• Session continuity is maintained across agent switches for seamless conversations

3. Context-Aware Personalization

Heroku's Managed Inference enables MentorMind to:

• Analyze students' study session data (duration, subjects, time of day) to identify optimal learning windows
• Process performance metrics to detect struggling areas and recommend targeted interventions
• Track conversation history to provide increasingly personalized advice over time
• Adapt recommendations based on real-time feedback and study outcomes

Technical Implementation

Frontend Architecture

• Framework: Next.js 14 with TypeScript and App Router
• UI Components: Shadcn/ui with Tailwind CSS styling
• Real-time Data: Convex React hooks for reactive queries

MentorMind (RAG-like Architecture)

• Multi-Model Support: Dynamic model selection (GPT-OSS 120B, Nova Lite/Pro, Claude 3.5 Haiku)
• Chat Interface: Real-time messaging with streaming responses and conversation history
• Context Retrieval: Fetches user study data, group information, and performance metrics from Convex backend
• Contextual Generation: AI models generate personalized responses using retrieved user data as context

export async function POST(req: Request) {
  try {
    const { messages, userName, studyStats, groupInfo, modelId }: AIRequestBody & { modelId?: string } =
      await req.json()

    const config = validateOpenAIConfig(modelId)
    const systemPrompt = buildSystemPrompt({ userName, studyStats, groupInfo })

    const chatMessages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = [
      { role: "system", content: systemPrompt },
      ...sanitizeMessages(messages),
    ]

    const client = createOpenAIClient(config)
    const completion = await client.chat.completions.create({
      model: config.herokuModelId,
      messages: chatMessages,
      ...DEFAULT_COMPLETION_OPTIONS,
    })

    const { choices, toolInvocations } = processAIResponse(completion)

    return Response.json({ ...completion, choices, toolInvocations })
  }

Backend Architecture

• Database: Convex with real-time subscriptions and optimistic updates
• Authentication: Convex Auth with secure session management
• API Routes: Next.js API routes for AI model communication
• Data Models: Structured schemas for study sessions, user settings, groups, and analytics

Challenges Solved

• Real-time State Synchronization: Optimistic updates with Convex for seamless UX across study sessions, todos, and group messaging
• Multi-Model AI Integration: Dynamic model switching with fallback handling and unified response processing
• Contextual AI Responses: Real-time data injection from Convex backend into AI prompts for personalized recommendations

Future Improvements

• mcp support for realtime time data retrieval
• code execution for realtime data retrieval

Many Students are improving their study habits and getting better results using this platform. By dividing the study into smaller sessions, they are able to focus better and get more done.