Why students use or not use generative AI: Student conceptions, concerns, and implications for engineering education
Year: 2024
Type: article
Abstract: Generative artificial intelligence (GenAI) technologies are believed to transform engineering education. However, it remains underexamined how engineering students choose to use GenAI or not, along with the reasons behind their choices. To fill this research gap, this study presents a natural experiment that examines student use or non-use of GenAI tools in engineering design tasks in an undergraduate course. In this experiment, the participants ( n = 403) were provided with unconstrained access to a GPT 4.0-empowered chatbot and were allowed to use it for their design projects voluntarily. Overall, 59.80 % of the students reported substantial use of GenAI in their design projects, and 40.20 % showed limited or no use. Those adopters used GenAI to aid idea generation and brainstorming, mediate discussions with instructors/TA, overcome non-technical expertise gaps, and optimize their design solutions. Conversely, non-adopters attributed their reluctance and rejection to inherent limitations in GenAI outputs, misalignment between GenAI functionalities and project needs, a lack of adaptation and prompt skills, and unclear benefits of GenAI use for personal growth. This study challenges the popular assumption of naturally active GenAI adoption among university students. It identifies three major factors—task characteristics, decision-maker characteristics, and context characteristics—that shape students' adoption of and interaction with GenAI. The findings highlight the need of establishing a consensus across various stakeholders (i.e., students, instructors, curriculum developers, policymakers, and others), while calling for adaptation and evidence-based decision-making in integrating GenAI tools into engineering education.
Source: Digital engineering.
Author: Yun Dai
Institution: Chinese University of Hong Kong
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,700
OpenAlex ID: W4404027933