About the Project

Inspiration

Students often choose courses using unstructured information such as word-of-mouth advice or lengthy online reviews that are difficult to interpret. At the same time, professors receive extensive feedback through course evaluations, yet identifying consistent and actionable insights requires significant manual effort. We were inspired by the idea that valuable academic feedback already exists, but current systems do not effectively transform it into usable knowledge. With recent advances in AI, we saw an opportunity to convert qualitative feedback into structured insights that support both student preparation and teaching improvement.

What We Built

We developed an AI-powered web application that analyzes professor and course reviews to generate clear, course-specific insights. Students can select a professor or course to receive concise summaries describing expectations, workload, and common learning challenges. The platform also provides a numerical professor score derived from aggregated feedback and an interactive chatbot that answers preparation-focused questions.

Rather than presenting isolated opinions, our system identifies patterns across many reviews, helping users better understand course experiences before enrollment.

How We Built It

Our system processes review data through a pipeline that cleans and structures unstructured text before applying large language models to summarize feedback and detect recurring themes:

[ \text{Unstructured Reviews} \rightarrow \text{AI Analysis} \rightarrow \text{Actionable Insights} ]

The frontend web application enables real-time interaction with AI-generated summaries and chatbot responses, creating an accessible interface for both students and educators.

Challenges

A key challenge was ensuring credibility when summarizing subjective reviews. Individual feedback can vary widely based on personal expectations, so we focused on extracting consistent trends rather than amplifying single perspectives. Integrating data processing with responsive AI interaction during live demonstrations also required careful backend coordination and interface design.

What We Learned & Future Vision

This project demonstrated how AI can transform existing feedback into decision-support tools. At scale, the platform could evolve into an academic intelligence system supporting institutional teaching analytics, curriculum development, and improved student preparedness. We envision a future where educational decisions are guided by collective learning insights rather than trial and error.

Built With

• cors
• css3
• express.js
• gpt-4o-mini
• html5
• javascript
• node.js
• openai
• python
• react
• requests
• selenium
• supabase
• vite