AI Biblio-metric Analysis

Jace Hargis
4 hours ago
3 min read

This week I would like to present a SoTL from the Canadian Journal of Learning and Tech entitled "Artificial Intelligence and University Education: Bibliometric Analysis of Research Trends and Perspectives" by Elharbaoui and Ntebutse (2025) (https://cjlt.ca/index.php/cjlt/article/view/28788/21210 ). The authors conducted a comprehensive bibliometric analysis of 285 peer-reviewed articles published between 2014 and March 2024 that explore the integration of AI in higher ed. Drawing on data from the Web of Science database, this investigation maps the evolution, collaboration networks, and dominant themes in scholarly research concerning AI’s pedagogical role.

Findings

The analysis reveals that AI-related research in higher ed surged after 2022, reflecting the broader academic community’s rapid engagement with tools like LLMs and adaptive learning systems. China and the US emerged as the most influential contributors, not only in publication volume but also in citation impact. Recent collaborations have expanded to include Canada, Israel, Brazil, South Africa, Singapore, and Vietnam, highlighting a growing internationalization of this research space.

Using visualization tools, the authors identified six thematic clusters centered around keywords like AI, motivation, self-regulated learning, feedback, and education. These clusters reflect an emphasis on student motivation, personalized learning, and performance prediction key indicators of AI’s pedagogical promise. However, less attention has been devoted to cognitive style and ethical challenges, suggesting emerging areas for further inquiry.

While AI applications ranging from chatbots for learner support to data-driven adaptive instruction enhance educational personalization, the study warns of significant ethical dilemmas. These include concerns about student data privacy, algorithmic bias, and the opacity of “black box” systems (Liu et al., 2024; Williams, 2024). Moreover, the authors caution that excessive automation could erode human interaction and intrinsic motivation, calling for balanced and transparent AI integration in pedagogical contexts.

The authors further argue that understanding AI’s integration into education requires interdisciplinary collaboration across computer science, educational psychology, and data ethics. They call for:

Expanded databases beyond Web of Science (Scopus) to improve representativeness.
Cross-cultural and qualitative studies to examine context-specific applications of AI in teaching and learning.
Ethical frameworks that ensure transparency, inclusivity, and fairness in AI-driven decision-making.

These steps are essential for building a more comprehensive and socially responsible understanding of AI’s transformative potential in higher ed.

References

Chai, C. S., Lin, P.-Y., Jong, M. S.-Y., Dai, Y., Chiu, T. K. F., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89–101.

Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with artificial intelligence (AI) based chatbot. Interactive Learning Environments, 32(7), 3240–3256. https://doi.org/10.1080/10494820.2023.2172044

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8

Elharbaoui, E., & Ntebutse, J. G. (2025). Intelligence artificielle et formation universitaire : analyse bibliométrique des tendances et perspectives de recherche. Canadian Journal of Learning and Technology, 51(1), 1–33.

Liu, A., Lin, Z., & Zhou, Y. (2024). Algorithmic transparency and educational ethics in AI-driven learning systems. Journal of Educational Computing Research, 62(3), 451–468.

Williams, D. (2024). Data ethics and student privacy in AI-enabled classrooms. British Journal of Educational Technology, 55(2), 367–384.