Our Artificial Intelligence Projects
Welcome to our collection of scholars’ chatbots! For us humans, a conversation is natural. It is part of our everyday life. We fundamentally understand it, and it's part of who we are. However, teaching a machine to have a conversation with people is hard because it is all about the conversation experience.
Sping 2026
This project focused on developing an AI-powered chatbot inspired by Dr. Hawa Abdi, a Somali physician, humanitarian, and human rights activist. Using Hugging Face Custom Chat Spaces, we combined biographical research with prompt engineering to create a chatbot that reflected her life, values, and contributions. Through multiple rounds of user testing, we refined its responses to improve accuracy, consistency, and authenticity while considering the ethical use of AI. This project demonstrated how artificial intelligence can be used to preserve the stories of influential women in STEM and make them more accessible. Through collaboration and iterative design, we strengthened our skills in AI development, research, communication, and teamwork. Learn more about our research here.
Sping 2026
This project focused on developing an AI-powered chatbot inspired by Dr. Virginia Apgar, the physician who created the Apgar Score for assessing newborn health. Using Hugging Face, we combined historical research with prompt engineering to build a chatbot that accurately reflected Dr. Apgar’s life, contributions, and perspective. Through multiple rounds of testing and refinement, we improved the chatbot’s accuracy, consistency, and educational value while ensuring its responses remained reliable and engaging. This project demonstrated how artificial intelligence can be used to preserve the legacies of influential STEM figures and create interactive learning experiences. Learn more about our research here.
Sping 2026
This project involved developing an AI-powered chatbot inspired by Katherine Johnson, the NASA mathematician whose calculations were instrumental to the success of early U.S. space missions. Using Python, Gradio, and Hugging Face Spaces, we combined historical research with personality prompt engineering to create an interactive chatbot that accurately reflected her life, achievements, and perspective. Through iterative testing and refinement, we improved the chatbot’s tone, accuracy, and user experience while grounding its responses in a curated knowledge base. The project highlights how artificial intelligence can be used to preserve the stories of pioneering women in STEM and make their contributions more engaging and accessible. Learn more about our research here.
Sping 2026
This project focused on developing an AI-powered chatbot inspired by Hedy Lamarr, an actress and inventor whose work on frequency-hopping technology laid the foundation for modern wireless communication. Using Hugging Face Custom Chat Spaces and personality prompt engineering, the chatbot was designed to simulate conversations that accurately reflected Lamarr’s voice, experiences, and contributions. Extensive historical research and multiple rounds of user testing were used to refine the chatbot’s responses, improve factual accuracy, and maintain consistency across a wide range of questions. The project demonstrates how artificial intelligence can preserve the legacies of influential women in STEM while creating engaging, interactive educational experiences. Learn more about our research here.
Spring 2025
Claude Shannon was an American mathematician and electrical engineer who is widely regarded as the father of information theory. His groundbreaking work laid the foundation for modern digital communication and data compression. Shannon's theories revolutionized the way information is transmitted, stored, and processed. He was a pioneer in applying mathematical principles to communication systems, shaping the future of technology and computing. Read about Sherina and Wintana’s experiences here.
Fall 2024
Rosalind Franklin was a British chemist and X-ray crystallographer who played a pivotal role in uncovering the helical structure of DNA. She made significant contributions to the study of DNA, RNA, viruses, coal, and graphite, paving the way for advancements in molecular biology.
Read about Dalal and Ghezal’s experiences here.
Fall 2023
Grace Hopper was a pioneering American computer scientist and United States Navy rear admiral who played a crucial role in the development of early computer programming languages, notably COBOL.
Read about Judy and Carole’s experience here.
Fall 2023
Ada Lovelace was an English mathematician and writer known for her collaborative work on the Analytical Engine, an early mechanical general-purpose computer, which made her the world's first computer programmer.
Read about Meba and June’s experience here.
Spring 2024
Mary Jackson was an American mathematician and aerospace engineer who became the first black woman to work at NASA. She was a leader in ensuring equal opportunities for the next generation of female scientists.
Read about Tina, Ester, and Menwa’s experiences here.
Fall 2023
Albert Einstein was a theoretical physicist known for developing the theory of relativity and making groundbreaking contributions to the understanding of quantum mechanics, winning the Nobel Prize in Physics in 1921.
Read about Najmah and Ester’s experience here.
Fall 2022
Rosalind Franklin was a British biophysicist and X-ray crystallographer whose critical contributions to the discovery of the DNA double helix structure laid the foundation for advancements in molecular biology.
Read about Heya and Hafssa’s experience here.
Fall 2025
This project created an interactive AI chatbot that simulates a Q&A interview with Katherine Johnson, using Google Cloud Dialogflow to bring her life, achievements, and legacy to life through conversational AI. The experience strengthened our skills in natural language processing, research synthesis, and project management while demonstrating how technology can make STEM history more engaging and accessible.
Read about Samantha Jeffers & Simiao Li’s experiences here.
Fall 2025
This project explored unsupervised learning through hierarchical clustering in R using the Palmer Penguins dataset to uncover natural patterns in biological data without predefined labels. By adapting clustering methods from a Fish dataset, we strengthened our understanding of hierarchical clustering techniques while learning to interpret species-level and habitat-related patterns from quantitative data. Read more here
Fall 2025
This project applied unsupervised learning with hierarchical clustering in R to analyze the Palmer Penguins dataset and uncover natural patterns in penguin species based on physical traits such as bill length, bill depth, flipper length, and body mass. The analysis revealed three well-defined clusters that closely aligned with Adelie, Gentoo, and Chinstrap species, demonstrating how computational methods can effectively identify biological patterns without prior labels. Read more here