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
TL;DR
Trust in GenAI among students is a key determinant of adoption and learning outcomes in CS education. The authors conduct a cross-site exploratory survey of 253 students at UH and IITK, combining quantitative trust measures with qualitative open-ended responses to examine GenAI use and its perceived impact on learning. They report a varied trust landscape (approximately $16\%$ distrust, $36\%$ neutral, $47\%$ trust) and identify a positive association between trust and motivation ($r\approx 0.57$) as well as programming confidence ($r\approx 0.40$), with stronger links for first-generation students ($r_{motivation}=0.57$, $r_{confidence}=0.40$) than continuing-generation peers ($r_{motivation}=0.37$, $r_{confidence}=0.35$). These findings offer practical guidance for educators and GenAI developers to calibrate trust and design educational interventions and interfaces that promote effective, supervised use of GenAI in CS coursework.
Abstract
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.