Using a Large Language Model-Powered Assistant in Teaching: Stories of Acceptance, Use, and Impact among Ethnic Minority Students - Erezi Ogbo-Gebhardt; Oruaro Ogbo (International Telecommunications Society (ITS), 2024)
Generative Artificial Intelligence (Gen AI), such as pre-trained Large Language Models (LLMs), is being introduced into society and is expected to transform various fields of human endeavor. Although researchers have explored how LLM-powered tools can assist in student learning, limited studies have been conducted on the adoption, acceptance, and use by ethnic minority students, a group often underserved and underrepresented in developing and deploying cutting-edge technologies. This study analyzes qualitative data from graduate students and recent alumni of a US Historically Black College and University. We implement an LLM-powered tool that leverages Retrieval Augmented Generation (RAG) grounded in their teaching material, then ask participants to complete tasks based on the material, with and without the presence of the tool. We conducted semi-structured interviews before and after the tasks to obtain first-hand feedback on using Gen AI tools in a practical educational scenario. This study sheds light on some of the expectations, use, and barriers to acceptance from ethnic minority groups and can inform future designs and implementations of Generative AI tools in pedagogical environments.
Keywords: 330; Generative Artificial Intelligence; Large Language Models; UTAUT; Digital Inclusion; HBCU; Pedagogy Technologies