Trust In Generative AI Among Students: An Exploratory Study
Matin Amoozadeh, David Daniels, Daye Nam, Aayush Kumar, Stella Chen, Michael Hilton, Sruti Srinivasa Ragavan, Mohammad Amin Alipour . Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 2024 – 61 citations
Generative artificial systems (GenAI) have experienced exponential growth in the past couple of years. These systems offer exciting capabilities, such as generating programs, that students can well utilize for their learning. Among many dimensions that might affect the effective adoption of GenAI, in this paper, we investigate students’ \textit{trust}. Trust in GenAI influences the extent to which students adopt GenAI, in turn affecting their learning. In this study, we surveyed 253 students at two large universities to understand how much they trust \genai tools and their feedback on how GenAI impacts their performance in CS courses. Our results show that students have different levels of trust in GenAI. We also observe different levels of confidence and motivation, highlighting the need for further understanding of factors impacting trust.