AI and Academic Integrity: A Bibliometric Analysis from the Scopus Database (2004-2024) and the Missing Voices of International Students

• In Event: Balancing Global Standards, Local Realities, and AI in Education

Proposal

The rise of artificial intelligence (AI) in higher education has raised significant concerns regarding academic integrity, especially as students increasingly utilize AI tools like generative AI for various academic purposes. This paper aims to explore the current research landscape on the intersection of AI and academic integrity among university students through a bibliometric analysis of publications from the Scopus database (2004-2024), which includes journal articles, books, book chapters, and conference proceedings.
The analysis provides an overview of trends in research output, geographic distribution, institutional affiliations, and key research topics. While AI technology is often framed as a tool for enhancing academic productivity, its unintended consequences - particularly for vulnerable groups like international students - require deeper scrutiny. These students, who often rely on AI to overcome language barriers, face unique challenges. However, even when AI tools are not used, a study by Liang et al. (2023) found that GPT detectors frequently misclassify non-native English writing as AI-generated, while accurately identifying native English writing. This misclassification, coupled with the oversimplification of "integrity" within these systems, not only undermines international students' efforts but also raises fairness concerns, potentially leading to unjust penalties or bans.
Moreover, as AI systems increasingly shape academic integrity policies, the needs and challenges faced by international students are frequently overlooked. Indeed, despite their significant economic contributions, the specific needs of these vulnerable students are often overlooked. Bannister et al. (2024), through a qualitative analysis of AI-related academic integrity policies from 131 higher education institutions across 11 countries, found that 96.95% of these policies fail to address the challenges faced by international students. Similarly, Sullivan et al. (2023) analyzed 100 news articles from the U.S., U.K., Australia, and New Zealand, highlighting the lack of public discussion on ChatGPT’s potential to support disadvantaged students, as well as the minimal representation of student voices in media coverage on this issue. This gap in both public discourse and policy highlights an emerging digital divide—where access to and the consequences of AI technologies differ greatly across student populations.
In response to this issue, this paper’s bibliometric review also examines the representation of international students within the literature on AI and academic integrity. Preliminary results indicate that research on international students comprises less than 3% of the overall 891 Scopus publications in this field, underscoring the need for more inclusive scholarship and policy-making. Furthermore, despite their significant presence in countries like the U.S., Canada, and France (which rank 1st, 3rd, and 4th worldwide, hosting 33% of global international students (Project Atlas, 2023)), these regions contribute relatively little to this area of research, raising concerns about global disparities in addressing this crucial issue.
This bibliometric analysis will offer recommendations for more inclusive approaches to academic integrity, addressing the ethical challenges AI poses in higher education, and ensuring all students are treated fairly. This underscores the urgent need for further research focused on international students' experiences and perspectives to ensure their voices are part of shaping AI-related academic integrity policies and practices.

References

Bannister, P., Alcalde Peñalver, E., & Santamaría Urbieta, A. (2024). International Students and Generative Artificial Intelligence: A Cross-Cultural Exploratory Analysis of Higher Education Academic Integrity Policy. Journal of International Students, 14(3), 149–170. 10.32674/jis.v14i3.6277
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. 10.1016/j.patter.2023.100779
Project Atlas. (2023). A Quick Look At Global Mobility Trends. (). https://www.iie.org/wp-content/uploads/2024/01/Project-Atlas_Infographic_2023_2.pdf
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning.

Author

• Ngoc Minh Kim, George Washington University