This individual-based project aimed to generate mind maps based on themes and connections from physics textbooks using NLP algorithms.
The project involved using natural language processing (NLP) algorithms to analyze the text of physics textbooks and extract key themes and concepts. The goal was to create a visual representation of the relationships between these themes and concepts of physic textbooks, allowing for a deeper understanding of the material. The mind maps generated from the analysis provided a unique way to visualize the connections between different topics in physics, making it easier for students to grasp complex concepts. This project showcased the potential of AI and NLP in education, particularly in enhancing learning experiences and improving comprehension of challenging subjects. I was responsible for implementing the NLP algorithms, analyzing the text data, and creating the mind maps. This experience allowed me to apply my programming skills in a practical setting and gain hands-on experience in AI and NLP. Additionally, I presented the results at the Spelman College Research Symposium and received 2nd place in the physics category. Overall, this project provided me with valuable insights into the applications of AI in education and the potential for technology to enhance learning experiences.