Week 8 and 9 –– Analyzing the Data and Finding Patterns

Weeks 8 and 9 focused mainly on analyzing the data collected from students using SimplrAI. After spending the previous weeks testing features and gathering feedback, I wanted to better understand how students actually study, what they struggle with most, and which parts of the platform were genuinely useful.

One of the biggest patterns I noticed was the difference between student usage in the United States and India. Students in India generally uploaded much larger amounts of material at once –– full chapters, long PDFs, and dense exam review notes. Many of them used SimplrAI during high-intensity study sessions, especially for competitive exams or cumulative tests. Their focus was usually speed, retention, and breaking down large amounts of information quickly.

Students in the United States, on the other hand, tended to use the platform more consistently over time. Instead of uploading huge batches of content, they often worked with lecture notes, homework material, or smaller class-based assignments. Their usage was more centered around understanding concepts gradually rather than rapid review.

Another interesting finding was how users interacted with explanations. I originally assumed longer, detailed outputs would be more valuable, but that was not always true. Most students preferred shorter, structured explanations with clear summaries and highlighted key concepts. Simplicity and readability mattered more than length and depth.

I also noticed that the chatbot became one of the most-used features because students naturally wanted to ask follow-up questions instead of just reading static explanations. This reinforced the idea that learning works better when it feels interactive.

These weeks helped me understand that the next step in developing SimplrAI is about adapting to different learning habits, academic systems, and study behaviors.