Designing AI Software for Large Classroom Engagement - Koehl, Stephanie (My University, 2025)
This thesis presents the design of an educational tool that enhances student engagement and interaction during group presentations in large classroom settings. Specifically, the study aimed to create a tool that streamlines the management of questions and participation, making the process more efficient and equitable for students and instructors. The research explored three primary questions: (1) How can educational software be designed to increase engagement and participation during student presentations? (2) How can AI be used to assist in tasks traditionally performed by professors, such as managing Q&A sessions? (3) How does the application of design thinking, particularly the empathy stage, influence the development of effective educational tools? Students provided ample feedback on improving the course and detailed explanations for their preferences. Qualitative methods including reflexive thematic analysis were used to process this volume of feedback. Descriptive statistics, confusion matrices, and Kappa scores were used to ensure the integrity of the analysis. An open-source large language model, Meta’s LLaMA, was implemented to automate the selection and clustering of questions during student-led Q&A sessions, with these results compared against instructor-selected questions. AI-driven question selection matched the effectiveness of instructor selections and enhanced efficiency, significantly reducing the logistical burden on educators while sustaining student engagement. Additionally, the research gathered extensive data on students’ experiences within the university classroom, with particular attention to issues such as anxiety, group dynamics, and disengagement. A paper prototype was developed to address these challenges, leveraging AI to foster interaction and improve peer-to-peer communication. These results have broader implications for educational technology, showing how AI could foster deeper student involvement and provide instructors with tools to manage participation effortlessly at scale, improving the overall learning experience.
INTERACTIVE LEARNING AT SCALE: LEVERAGING GENERATIVE AI TO IMPROVE ENGAGEMENT AND PARTICIPATION IN LARGE CLASSROOM SETTINGS
Keywords: Design Thinking; Generative Artificial Intelligence; Software Design; AI in Education; Large Language Models; Educational Software