Autonomous driving paper index
Research on the factors influencing the adoption of AIGC for design assistance among design students in higher education institutions: an extended UTAUT model
One-line summary
To this end, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT), integrates individual needs for work creativity, perceived ethical risks, artificial intelligence (AI) ethical anxiety, and trust to construct a new research framework.
Engineering notes
Results from structural equation modeling analysis indicate: First, performance expectancy and trust significantly influence design students’ adoption of AIGC, with trust exerting the strongest influence.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
In recent years, Artificial intelligence generated content (AIGC) technology has demonstrated significant application potential in the fields of design, art, and education, particularly in aiding design creation and enhancing production efficiency. However, at the same time, there are still controversies surrounding its ethical risks, trustworthiness, and technical adaptability. In design programs at higher education institutions, AIGC is gradually becoming an important tool for assisting design creation. To this end, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT), integrates individual needs for work creativity, perceived ethical risks, artificial intelligence (AI) ethical anxiety, and trust to construct a new research framework. The aim is to reveal the impact of these factors on the behavioral intention of design students in higher education institutions to adopt AIGC. A total of 376 valid data were collected via questionnaire. Results from structural equation modeling analysis indicate: First, performance expectancy and trust significantly influence design students’ adoption of AIGC, with trust exerting the strongest influence. Second, effort expectancy, facilitating conditions, and job creativity requirement positively promote the intention to use AIGC. Lastly, perceived ethical risks and AI ethical anxiety do not directly affect adoption intentions but exert indirect effects through trust. This study not only provides a new perspective for the extension of UTAUT but also offers practical recommendations for educators and technology developers in promoting the application of AIGC.
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