Exploring Engineering Students’ Attitudes and AI Preparedness: Insights from a Cross-Cultural Educational Study in China
This study examines the attitudes and expectations of Chinese engineering students through a comprehensive survey conducted during a Modern Technologies course taught at Hubei Normal University in Summer 2024. The research investigated students' perspectives on STEM fields, emerging technologies, and particularly artificial intelligence (AI), gathering data from 118 participants through both quantitative and qualitative methods.Results revealed strong family and peer influence in students' choice of engineering careers, with over 50% approval rates for propositions related to engineering career value and self-efficacy. Students reported increased engagement with professional news sources. Course interventions improved students’ confidence in English communication and critical thinking, while AI translation tools facilitated cross-cultural education.Students expressed strong interest in robotics and AI-related careers, though some expressed concerns about skill gaps and entry barriers. We identified a need for enhanced AI education in undergraduate curricula and demonstrated the success of incorporating real-world technology examples, such as robotic demonstrations, in engaging students.The study concludes with recommendations for incorporating AI ethics and emerging technologies into engineering education, while highlighting the importance of cross-cultural academic exchanges between the United States and China. These findings contribute to understanding international engineering education needs and can inform curriculum development for global audiences.
Authors
- Yonghe Wang (ORCID: https://orcid.org/0000-0003-1647-1539)
- Jiacheng Yuan
- A. Ravishankar Rao (ORCID: https://orcid.org/0000-0002-4810-0241)
- Jiawei Lin
Institutions
- Fairleigh Dickinson University (US)
- Hubei Normal University (CN)
Publication Details
- Published
- 2025-03-15
- DOI
- https://doi.org/10.1109/isec64801.2025.11147275
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.69