Trust in Generative AI among Students: An exploratory study
Open accessAbstract
Generative Artificial Intelligence (GenAI) systems have experienced exponential growth in the last couple of years. These systems offer exciting capabilities for CS Education (CSEd), such as generating programs, that students can well utilize for their learning. Among the many dimensions that might affect the effective adoption of GenAI for CSEd, in this paper, we investigate students' trust. Trust in GenAI influences the extent to which students adopt GenAI, in turn affecting their learning. In this paper, we present results from a survey of 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.
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Recent studies indicate that college students perceive potential credibility issues with GenAI-generated information but lack the necessary skills to verify its accuracy.
“Recent studies have indicated that college students perceive potential credibility issues with GenAI-generated information but lack the necessary skills to verify its accuracy.”
From College students' credibility assessments of <scp>GenAI</scp>‐generated information for academic tasks: An interview study (2025) · cites this paper as Amoozadeh et al., 2024
Blind distrust risks depriving students of opportunities to benefit from GenAI tools for academic tasks.
“If blind trust is one side of the coin, blind distrust is the other, which risks depriving students of opportunities to benefit from GenAI tools for academic tasks (Amoozadeh et al., 2024).”
From College students' credibility assessments of <scp>GenAI</scp>‐generated information for academic tasks: An interview study (2025) · cites this paper as Amoozadeh et al., 2024
Some students expressed concerns about the reliability and accuracy of ChatGPT's outputs.
“However, some students also expressed concerns about the reliability and accuracy of ChatGPT's outputs, aligning with findings from other user studies (Amoozadeh et al. 2024;Ding et al. 2023;Malik et al. 2023;Zhao et al. 2024).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Amoozadeh et al. 2024
Many students worried about the reliability and accuracy of ChatGPT's outputs, citing issues such as inaccuracies, inconsistencies, and the inability to handle complex subjects.
“Many worried about the reliability and accuracy of ChatGPT's outputs, citing issues such as inaccuracies, inconsistencies, and the inability to handle complex subjects (Amoozadeh et al. 2024;Ding et al. 2023;Malik et al. 2023;Zhao et al. 2024).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Amoozadeh et al. 2024
An AI-literate teacher's trust in GenAI's consistent performance determines whether they perceive it as genuinely useful.
“An AI-literate teacher might understand the potential of GenAI, but it is their trust in the tool's consistent performance that will determine if they perceive it as genuinely useful [5,56].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [56]
Lack of trust stemming from an insufficient understanding of AI operations contributes to resistance among teachers towards the integration of AI-based tools into their instructional practices.
“The lack of trust stemming from an insufficient understanding of AI operations contributes to resistance among teachers towards the integration of AI-based tools into their instructional practices [5,56].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [56]
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“However, the use of GenAI also comes with its drawbacks for education, notably over concerns of plagiarism, potential biases, dealing with inaccurate results from GenAI, and the potential development of unfavorable habits among students [31].”
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“For example, Amoozadeh et al. [7] conducted an exploratory study explicating university students’ trust in GenAI, while Kim et al. [63] found that subpar responses from GenAI significantly decreased users’ trust.”
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- Authors
- Amoozadeh, M., Daniels, D., Nam, D., Kumar, A., Chen, S., Hilton, M., Srinivasa Ragavan, S., & Alipour, M. A.
- Affiliations
- University of Houston (US)
- Simon Fraser University (CA)
- Carnegie Mellon University (US)
- Indian Institute of Technology Kanpur (IN)
- Indian Institute of Technology, Kanpur, India
- ORCID
- Venue
- Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
- Publisher
- ACM
- Published
- 2024-03-07
- Pages
- 67-73
- OpenAlex
- W4392542324
- Cited by
- 143
- References
- 5
- Citations by year
- 2026: 41 · 2025: 72 · 2024: 30
- FWCI
- 54.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (2225373)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, trust in AI, computer science education, student trust, artificial intelligence adoption, student self-confidence, student motivation, AI-generated code, academic performance
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