Engineering Students’ Use of Large Language Model Tools: An Empirical Study Based on a Survey of Students from 12 Universities DOI Creative Commons
Rongsheng Li, Manli Li, Weifeng Qiao

и другие.

Education Sciences, Год журнала: 2025, Номер 15(3), С. 280 - 280

Опубликована: Фев. 24, 2025

Large language model (LLM) tools, such as ChatGPT, are rapidly transforming engineering education by enhancing tasks like information retrieval, coding, and writing refinement, which critical to the problem-solving technical focus of disciplines. This study investigates how students use LLM tools challenges they face, offering insights into adoption AI technologies in academic settings. A survey 539 from 12 leading Chinese universities, using UTAUT framework, examines factors technological expectations, environmental support, personal characteristics. The key findings include following: (1) Over 40% with 18.8% regarding them indispensable. (2) Trust AI-generated content remains a central challenge, must critically evaluate its accuracy reliability. (3) Environmental support significantly affects usage, notable regional disparities, particularly between eastern other regions China. (4) persistent digital divide, influenced gender, level, socioeconomic background, depth effectiveness tool use. These results underscore need for targeted address demographic disparities optimize integration education.

Язык: Английский

Engineering Students’ Use of Large Language Model Tools: An Empirical Study Based on a Survey of Students from 12 Universities DOI Creative Commons
Rongsheng Li, Manli Li, Weifeng Qiao

и другие.

Education Sciences, Год журнала: 2025, Номер 15(3), С. 280 - 280

Опубликована: Фев. 24, 2025

Large language model (LLM) tools, such as ChatGPT, are rapidly transforming engineering education by enhancing tasks like information retrieval, coding, and writing refinement, which critical to the problem-solving technical focus of disciplines. This study investigates how students use LLM tools challenges they face, offering insights into adoption AI technologies in academic settings. A survey 539 from 12 leading Chinese universities, using UTAUT framework, examines factors technological expectations, environmental support, personal characteristics. The key findings include following: (1) Over 40% with 18.8% regarding them indispensable. (2) Trust AI-generated content remains a central challenge, must critically evaluate its accuracy reliability. (3) Environmental support significantly affects usage, notable regional disparities, particularly between eastern other regions China. (4) persistent digital divide, influenced gender, level, socioeconomic background, depth effectiveness tool use. These results underscore need for targeted address demographic disparities optimize integration education.

Язык: Английский

Процитировано

0