Neural Prof


Transform PDFs into Interactive Lessons: Your Study Companion

NeuralProf is an innovative web application developed during the StormHacks 2023 hackathon. Its primary purpose is to revolutionize the learning experience for university students by leveraging artificial intelligence and natural language processing. With NeuralProf, students can easily upload PDF documents related to their courses. The app extracts the text content from the PDFs and generates a comprehensive and personalized step-by-step lesson plan based on the extracted information. The lesson plan breaks down complex topics into manageable sections, allowing students to follow along more effectively. One of the standout features of NeuralProf is its interactive question and answer system. At each step of the lesson plan, students can ask questions based on the PDF data, and the app utilizes the OpenAI Python API to provide accurate and relevant answers. This feature promotes active learning and helps students clarify any doubts they may have while studying. Furthermore, NeuralProf offers a built-in quiz functionality. Students can test their understanding of the material covered in each step by taking quizzes generated from the extracted content. This gamified approach fosters engagement and reinforces learning outcomes.

NeuralProf draws inspiration from the challenges faced by university students in managing vast amounts of course material and enhancing their understanding of complex topics. It aims to provide a centralized platform that simplifies the learning process, making it easier for students to digest and grasp essential concepts.

The app is inspired by the pedagogical methods used in traditional university lessons, where content is organized into step-by-step plans. By emulating this structure, NeuralProf aims to create a familiar and intuitive learning environment that aligns with students' expectations and maximizes their educational outcomes.

The challenge of structuring lessons from the text-based responses generated by the OpenAI API was a significant hurdle I encountered while developing NeuralProf. Converting the continuous text into organized and coherent step-by-step lessons required careful processing and analysis. I implemented techniques such as sentence segmentation, semantic analysis, and content rearrangement to transform the text-based responses into structured lessons. Through iterative refinement and thorough testing, I successfully overcame this challenge and enabled NeuralProf to generate well-organized lesson plans based on the extracted information. This enhancement significantly improved the learning experience for users, allowing them to easily follow the structured lessons and grasp the material more effectively.

  • Python: Demonstrated strong proficiency in the Python programming language throughout the development of NeuralProf. Utilized Python's extensive libraries and frameworks to implement various functionalities and algorithms, ensuring efficient and effective code execution. Leveraged Python's readability, versatility, and robust ecosystem for rapid application development.
  • OpenAI API: Leveraged the OpenAI Python API extensively within NeuralProf to harness powerful natural language processing capabilities. Utilized the API to analyze and process the text extracted from PDFs, generating insightful and informative lesson plans. Incorporated features like interactive question and answer systems using the OpenAI API, enhancing the interactivity and educational value of the web app.
  • React: Developed the frontend of NeuralProf using the React JavaScript library. Leveraged React's component-based architecture to create an interactive and responsive user interface. Utilized React's virtual DOM and state management to efficiently update and render components, resulting in a smooth user experience. Employed React's reusability and modularity to create intuitive and user-friendly interfaces for the web app.
  • PDF Parsing: Implemented PDF parsing functionality to extract text content from uploaded PDF documents. Utilized Python libraries such as PyPDF2 or pdfminer to extract the raw text, overcoming challenges related to varying PDF layouts and formatting.
  • Natural Language Processing: Utilized natural language processing techniques to analyze and process the extracted text. Applied techniques such as named entity recognition, part-of-speech tagging, and semantic analysis to identify key concepts, topics, and transitions within the lesson material.
  • Interactive User Experience: Leveraged the OpenAI API to create an interactive learning experience for users. Incorporated features that allowed users to ask questions based on the PDF data and receive accurate and relevant answers. Implemented a quiz feature to test students' understanding of the material covered in each step, fostering engagement and reinforcing learning outcomes.