Artificial Intelligence in Higher Education: A Cross-Cultural Examination of Students’ Behavioral Intentions and Attitudes

Artificial intelligence (AI) has undergone considerable advancement in the contemporary period and represents an emerging technology in higher education. Cultural contexts significantly shape individuals’ perceptions, attitudes, and behaviors, particularly in the realm of technology acceptance. By adopting a cross-cultural lens, this research explores the potential variations across Chinese and international students from diverse countries in terms of attitudes and their behavioral intentions toward AI use. With a technology acceptance model (TAM) framework, the research used a survey approach, employing questionnaires as the primary means of data collection. The data were then analyzed through structural equation modeling and descriptive statistics. A substantial discrepancy was found in the prevalence, attitudes, and behavioral intentions toward AI use between Chinese and international students. Findings further revealed a stronger effect of perceived ease of use on both attitudes and behavioral intentions among international students compared with their Chinese counterparts. Findings suggest that cultural backgrounds and prior technological exposure play intricate roles in shaping perceptions of AI technology. The study emphasizes the need for tailored educational strategies to regulate diverse cultural perspectives, provide language-specific support, and ensure user-friendly interfaces. These insights contribute to the evolving discourse on technology acceptance in higher education and offer practical implications for educators and institutions toward optimizing AI integration in pedagogical practices.

Authors

Institutions

Publication Details

Journal
The International Review of Research in Open and Distributed Learning
Published
2024-08-26
DOI
https://doi.org/10.19173/irrodl.v25i3.7703
Citations
61
Primary Topic
Innovations in Education and Learning Technologies
Type
article
Field-Weighted Citation Impact
47.53
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence in Higher Education: A Cross-Cultural Examination of Students’ Behavioral Intentions and Attitudes

Huma Akram, Dongmin Ma, Chen Hua
61 citations
The International Review of Research in Open and Distributed Learning
Innovations in Education and Learning Technologies
47.53
article

Artificial Intelligence in Higher Education: A Cross-Cultural Examination of Students’ Behavioral Intentions and Attitudes

Huma Akram, Dongmin Ma, Chen Hua
article en
61 citations

Abstract

Artificial intelligence (AI) has undergone considerable advancement in the contemporary period and represents an emerging technology in higher education. Cultural contexts significantly shape individuals’ perceptions, attitudes, and behaviors, particularly in the realm of technology acceptance. By adopting a cross-cultural lens, this research explores the potential variations across Chinese and international students from diverse countries in terms of attitudes and their behavioral intentions toward AI use. With a technology acceptance model (TAM) framework, the research used a survey approach, employing questionnaires as the primary means of data collection. The data were then analyzed through structural equation modeling and descriptive statistics. A substantial discrepancy was found in the prevalence, attitudes, and behavioral intentions toward AI use between Chinese and international students. Findings further revealed a stronger effect of perceived ease of use on both attitudes and behavioral intentions among international students compared with their Chinese counterparts. Findings suggest that cultural backgrounds and prior technological exposure play intricate roles in shaping perceptions of AI technology. The study emphasizes the need for tailored educational strategies to regulate diverse cultural perspectives, provide language-specific support, and ensure user-friendly interfaces. These insights contribute to the evolving discourse on technology acceptance in higher education and offer practical implications for educators and institutions toward optimizing AI integration in pedagogical practices.

The International Review of Research in Open and Distributed LearningVol. 25(3)
North China University of Water Resources and Electric Power (CN), Qufu Normal University (CN)
Quality Education
Openalex Percentile: Top 0%
Innovations in Education and Learning Technologies
47.53
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.