Trust in Generative AI among Students

被引：44

作者：

Amoozadeh, Matin ^{[1
]}

Daniels, David ^{[2
]}

Nam, Daye ^{[3
]}

Kumar, Aayush ^{[4
]}

Chen, Stella ^{[1
]}

Hilton, Michael ^{[3
]}

Ragavan, Sruti Srinivasa ^{[4
]}

Alipour, Mohammad Amin ^{[1
]}

机构：

[1] Univ Houston, Houston, TX 77204 USA

[2] Simon Fraser Univ, Vancouver, BC, Canada

[3] Carnegie Mellon Univ, Pittsburgh, PA USA

[4] Indian Inst Technol, Kanpur, India

来源：

PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1 | 2024年

基金：

美国国家科学基金会;

关键词：

Generative AI; Trust; Novice programmers;

D O I：

10.1145/3626252.3630842

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

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.

引用

页码：67 / 73

页数：7

共 39 条

[1]

Amoozadeh Matin, 2023, P 2023 ACM C INT COM, V2, P3

[2]

Bang Y, 2023, Arxiv, DOI [arXiv:2302.04023, 10.48550/arXiv.2302.04023]

[3] Grounded Copilot: How Programmers Interact with Code-Generating Models [J].

Barke, Shraddha ;

James, Michael B. ;

Polikarpova, Nadia .

PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL, 2023, 7 (OOPSLA)

[4] Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI [J].

Barredo Arrieta, Alejandro ;

Diaz-Rodriguez, Natalia ;

Del Ser, Javier ;

Bennetot, Adrien ;

Tabik, Siham ;

Barbado, Alberto ;

Garcia, Salvador ;

Gil-Lopez, Sergio ;

Molina, Daniel ;

Benjamins, Richard ;

Chatila, Raja ;

Herrera, Francisco .

INFORMATION FUSION, 2020, 58 :82-115

[5] Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation [J].

Becker, Brett A. ;

Denny, Paul ;

Finnie-Ansley, James ;

Luxton-Reilly, Andrew ;

Prather, James ;

Santos, Eddie Antonio .

PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023, 2023, :500-506

[6] AI in education: learner choice and fundamental rights [J].

Berendt, Bettina ;

Littlejohn, Allison ;

Blakemore, Mike .

LEARNING MEDIA AND TECHNOLOGY, 2020, 45 (03) :312-324

[7]

Cheng RJ, 2022, Arxiv, DOI arXiv:2212.03491

[8]

Corbin J., 2008, Basics of qualitative research: Techniques and procedures for developing grounded theory, V3rd, DOI [10.4135/9781452230153, DOI 10.4135/9781452230153]

[9]

DeAngelo L., 2011, COMPLETING COLL ASSE

[10]

Denny P, 2023, Arxiv, DOI [arXiv:2306.02608, DOI 10.48550/ARXIV.2306.02608, 10.48550/arXiv.2306.02608]

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