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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Engineering Students’ Attitudes and AI Preparedness: Insights from a Cross-Cultural Educational Study in China

Yonghe Wang, Jiacheng Yuan, A. Ravishankar Rao, Jiawei Lin
1 citations
Online Learning and Analytics
1.69
article

Exploring Engineering Students’ Attitudes and AI Preparedness: Insights from a Cross-Cultural Educational Study in China

Yonghe Wang, Jiacheng Yuan, A. Ravishankar Rao, Jiawei Lin
article en
1 citations

Abstract

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.

Fairleigh Dickinson University (US), Hubei Normal University (CN)
Openalex Percentile: Top 10%
Online Learning and Analytics
1.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Exploring Engineering Students’ Attitudes and AI Preparedness: Insights from a Cross-Cultural Educational Study in China — Yonghe Wang, Jiacheng Yuan, et al. · (2025) | TGRS Research Map | TGRS