CS Student Copilot is a multi-agent AI system designed to assist computer science students with coding, studying, and academic research. The system employs a coordinated architecture to route user requests to specialized AI agents, each optimized for specific educational tasks. This project demonstrates practical applications of multi-agent architectures in educational technology.
Modern computer science education presents students with an increasingly complex landscape of learning challenges. While pursuing their studies, students must simultaneously master programming concepts, manage vast amounts of course materials, and stay current with rapidly evolving research literature. This creates several critical pain points that hinder effective learning.
Students often struggle with coding assignments, spending hours debugging syntax errors or searching for implementation examples across fragmented online resources. Meanwhile, their personal study materials—lecture notes, textbooks, and documentation—remain trapped in static formats, making it difficult to quickly locate specific information or connect concepts across different sources.
Academic research presents another significant hurdle. Students face the daunting task of navigating extensive paper databases, often spending more time searching for relevant literature than actually reading and understanding it. The process of discovering, accessing, and organizing academic papers becomes a barrier to deeper learning rather than a pathway to knowledge.
Perhaps most frustratingly, these challenges require students to constantly switch between multiple tools and platforms—code editors, document viewers, search engines, and academic databases—fragmenting their workflow and breaking their focus. This context-switching overhead reduces productivity and creates unnecessary cognitive load during critical learning moments.
What's needed is an integrated solution that understands the interconnected nature of computer science learning, providing intelligent assistance across coding, studying, and research activities within a unified, seamless experience.
The system implements a coordinator pattern where a central routing system directs tasks to three specialized AI agents:
graph TD A[User Input] --> B[Task Coordinator] B --> C[CodeHelper Agent] B --> D[StudyBuddy Agent] B --> E[ScholarScout Agent] C --> F[Code Generation & Analysis] D --> G[RAG-Based Q&A] E --> H[Academic Paper Discovery] F --> M[CLI Output / Streamlit Web App] G --> M[CLI Output / Streamlit Web App] H --> M[CLI Output / Streamlit Web App] M --> N[User Receives Response]
# Code assistance python3 main_cli.py codehelper "Implement quicksort algorithm in Python" # Study support python3 main_cli.py studybuddy ask "Explain machine learning concepts from my notes" # Research assistance python3 main_cli.py scholarscout "Find recent papers on transformer architectures"
streamlit run app_streamlit.py # Access at http://localhost:8501
git clone https://github.com/DimGiagias/cs_student_copilot.git cd cs_student_copilot # For Linux/macOS users: ./setup.sh --uv # Using uv package manager # or ./setup.sh # Using pip # For Windows users or manual installation: pip install -r requirements.txt
cp .env.example .env # Add OpenRouter API key to .env file ollama pull mxbai-embed-large # Setup embedding model
CS Student Copilot represents an initial exploration into addressing the fragmented nature of computer science education through AI. Built as a university coursework project, this implementation demonstrates one possible approach to integrating code assistance, document-based learning, and academic research into a single system.
While the project is still in its early stages and has room for improvement, it successfully shows how multi-agent architecture can be applied to educational challenges. The modular design provides a foundation that could be extended with better algorithms, more sophisticated routing, or additional capabilities as the project matures.
The main contribution lies in proving that these different educational tasks—coding help, document querying, and research assistance—can work together within a unified interface. Even with its current limitations, the system demonstrates the potential value of integrated learning tools over fragmented workflows.
From a technical perspective, the project serves as a practical example of multi-agent system design and RAG implementation. The clean separation between components makes it a useful reference for others interested in building similar educational AI tools or exploring agent-based architectures.
As a student project exploring AI applications in education, CS Student Copilot offers a starting point for further development and experimentation. The challenges of modern computer science education are complex, and solutions will likely emerge through iterative development and community collaboration rather than any single implementation.