CampusIQ

GitHub Repo: https://github.com/masirmohammadjave001/Thunderhacks.git CampusIQ was inspired by a common challenge faced by university students: navigating multiple disconnected systems to manage their academic life. At Algoma University, students often have to visit different websites and platforms to find information about courses, scholarships, financial aid, transcripts, and advising. This fragmentation can make it difficult for students to quickly find reliable information when they need it most. Our team wanted to explore how artificial intelligence could simplify this process by creating a unified assistant that understands both university resources and a student’s own academic documents. Instead of building a generic chatbot, we focused on creating a system that provides evidence-backed answers by combining official Algoma University information with student-specific context such as transcripts.

CampusIQ is an AI-powered student intelligence platform designed to help students navigate academic planning, financial aid opportunities, and administrative processes more easily. The platform features an AI advisor that allows students to ask questions about topics such as courses, scholarships, financial aid, and registrar services. In addition to answering general questions using official Algoma resources, the system can also analyze uploaded academic documents such as transcripts to provide more personalized guidance. For example, the chatbot can consider completed courses when discussing potential academic pathways or highlight financial aid opportunities based on the student’s profile. A key feature of the system is that responses are grounded in real sources, meaning the assistant provides supporting evidence from either university documents or the student’s uploaded materials to help ensure transparency and reliability.

To build CampusIQ, we developed a full-stack web application using modern technologies. The frontend was built with Next.js and React to create a responsive and user-friendly interface, while Tailwind CSS was used to design a clean layout and implement a floating AI chat assistant. On the backend, we implemented API routes that process student questions, retrieve relevant information from curated Algoma resources, and combine that information with document context when available. The AI component uses a retrieval-augmented approach, where the system first identifies relevant excerpts from official resources or uploaded documents and then provides those excerpts as context to the language model before generating a response. This method helps ensure that the chatbot remains grounded in real information rather than producing generic or unsupported answers. Additionally, uploaded PDF documents such as transcripts are parsed so that relevant academic details can be used to personalize the assistant’s guidance.

During development, our team faced several challenges. One of the biggest challenges was balancing the goal of building a realistic AI assistant with the limited time available during a hackathon. Creating a system that could retrieve information from university resources while also incorporating uploaded documents required careful design and coordination between different components of the application. Another challenge was ensuring that the AI responses remained accurate and supported by reliable sources. Language models can sometimes generate convincing but incorrect answers, so we had to structure our system in a way that prioritizes official Algoma information and document evidence. We also had to coordinate development across multiple team members who were working on different parts of the platform, including the student interface, administrative dashboard, and AI assistant features. Managing scope and ensuring that the core functionality worked smoothly within the time constraints was an important part of the process.

Through this project, we learned a great deal about building AI-assisted applications and integrating them into real-world systems. We gained practical experience designing retrieval-based AI systems that combine language models with structured data sources, which is an important technique for making AI assistants more reliable. We also learned how to structure a full-stack application that integrates frontend interfaces, backend APIs, and AI services into a cohesive platform. Working on CampusIQ also highlighted the importance of grounding AI responses in trusted sources, particularly when building tools intended to support students in making academic or financial decisions. Finally, the project provided valuable experience in teamwork and rapid prototyping within a hackathon environment, where collaboration, adaptability, and prioritizing the most impactful features are critical to success.