Week 2 — Expanding Inputs and Improving the Workflow

During Week 2, I focused on expanding what SimplrAI can actually accept as input. In Week 1, the system mainly worked with pasted notes. That was a good starting point, but it wasn’t realistic for how students actually gather information. Most of the time, content comes from multiple sources—web pages, documents, lecture slides, and other materials. So this week was about making SimplrAI more flexible.

One of the biggest features I added was link-based content extraction. Now, instead of manually copying text, a user can paste a web link and SimplrAI will extract the main content from that page. This allows students to pull information directly from online articles, course resources, or research pages and process it through the system.

I also implemented Word document support. Students often download lecture notes or assignments as .docx files, so SimplrAI can now read those files and extract the text automatically. Once the content is extracted, it goes through the same processing pipeline as regular notes—generating simplified explanations and identifying key concepts.

Another improvement I worked on was cleaning and structuring the extracted text before it goes into the AI processing stage. Raw web pages and documents often contain extra formatting, headings, or irrelevant content, so I added a step that filters and organizes the text before analysis. This makes the explanations generated by SimplrAI more accurate and easier to follow.

By the end of Week 2, SimplrAI became much more practical to use. Instead of only working with manually typed notes, it can now pull information directly from links and documents, which moves the platform closer to how students actually study and gather information.