CodeCampus AI

An AI-powered programming mentor that helps students learn, practice, debug, and prepare for interviews—all inside Slack.

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Inspiration

As an engineering student, I often found myself switching between multiple platforms while studying—Slack for communication, ChatGPT for explanations, YouTube for tutorials, and coding platforms for practice. This constant context switching disrupted my focus and made learning less efficient.

I wanted to create a tool that brings learning directly into the platform students already use to collaborate. That idea led to CodeCampus AI, an AI-powered learning companion built inside Slack.

What it does

CodeCampus AI helps students learn and practice programming concepts without leaving Slack. Users can interact with the bot using simple commands to:

1.Learn programming concepts with intuitive explanations and Java examples.

  1. Review code and receive improvement suggestions.
  2. Debug errors with step-by-step guidance.
  3. Solve coding challenges.
  4. Practice technical interview questions.
  5. Generate revision flashcards.
  6. Explore career roadmaps for different software roles.

The goal is to make learning interactive, organized, and accessible within a familiar workspace.

How we built it

The project was built using:

Python Slack Bolt SDK Google Gemini API Slack Socket Mode python-dotenv for secure API key management

The application listens for Slack mentions, identifies the requested command, selects an appropriate prompt template, sends the request to Gemini, and formats the AI-generated response before posting it back to Slack.

Challenges we ran into

The biggest challenges were integrating Slack with the Gemini API, designing prompts that produced consistent educational responses, and handling API limits while testing multiple features. I also learned how to organize a Python project, manage environment variables securely, use Git and GitHub effectively, and debug integration issues across different services.

These challenges taught me the importance of building incrementally, testing frequently, and keeping the project scope realistic for a hackathon.

Accomplishments that we're proud of

  • Successfully built a fully functional AI-powered Slack application from scratch.
  • Integrated Google Gemini AI with Slack to provide intelligent, context-aware learning assistance.
  • Designed multiple educational workflows, including concept learning, code review, debugging, coding challenges, interview preparation, revision flashcards, and career guidance.
  • Created a clean and modular Python codebase using Slack Bolt and prompt-based architecture.
  • Learned and applied Git and GitHub to manage and publish the project.
  • Turned an idea into a working prototype within the hackathon timeline while overcoming API integration, debugging, and prompt engineering challenges.

What we learned

Through this project, I gained hands-on experience with:

Building Slack applications using Slack Bolt. Integrating Large Language Models through the Google Gemini API. Prompt engineering for educational AI assistants. Managing API keys securely using environment variables. Using Git and GitHub for version control. Structuring a Python project for maintainability.

Most importantly, I learned how to take an idea from concept to a working prototype under a tight deadline.

What's next for CodeCampus AI

After the hackathon, I plan to expand CodeCampus AI with features such as:

  1. PDF and document-based learning support.
  2. Personalized learning progress tracking.
  3. Interactive quizzes and adaptive practice sessions.
  4. Team study and collaborative learning features.
  5. Smarter revision workflows powered by AI.

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