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Knowledge Mapping and Evolutionary Analysis of Artificial Intelligence Research in Higher Education: A Systematic Approach Using CiteSpace

Knowledge Mapping and Evolutionary Analysis of Artificial Intelligence Research in Higher Education: A Systematic Approach Using CiteSpace

Jianbo Huang (Jiyang College, Zhejiang Agriculture and Forestry University, China) and Kunpeng Cui (Jiyang College, Zhejiang Agriculture and Forestry University, China)
Copyright: © 2025 |Pages: 15
DOI: 10.4018/IJITSA.377176
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Abstract

The rapid evolution of Artificial Intelligence (AI) within higher education has prompted significant research into its integration and impact. This paper explores the development and current trends in AI research in higher education, utilizing CiteSpace to construct a comprehensive knowledge map. By analyzing multidimensional data such as keywords, authors, institutions, and journals, the study identifies key research hotspots and emerging trends. Findings indicate a rising publication trajectory, with Price's Law suggesting the lack of a consistent core group of influential contributors. Topics such as AI systems, adaptive learning models, big data integration, and intelligent environments are currently prevalent, reflecting shifts in research priorities. The paper offers insights into the systemic growth and structural evolution of AI in education, providing a foundation for advancing innovative educational ecosystems.
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Literature Review

The integration of AI in higher education has garnered significant attention in recent years, reflecting the broader trends in technology adoption across various sectors. This literature review synthesizes key findings from recent studies, focusing on the role of AI in enhancing educational practices, the challenges faced by institutions, and the implications for teaching and learning.

The Role of AI in Higher Education

AI technologies have been recognized for their potential to transform educational environments. University libraries are leveraging big data technologies to analyze academic research data and reader behavior. This integration aims to create knowledge innovation service platforms that enhance the discovery and recommendation of academic resources. The proposed functional model incorporates real-time data analysis and semantic web technologies, thereby facilitating personalized learning experiences and improving the overall efficiency of library services (Delcea et al., 2024; Song & Wang, 2020).

Moreover, the application of AI extends beyond library services. There is a growing necessity for college educators to develop intelligent teaching capabilities that encompass subject knowledge, technological proficiency, and pedagogical skills. This shift is crucial in addressing the challenges posed by the evolving educational landscape, particularly in the context of generative AI and its implications for moral education. Educators must reconstruct their teaching methodologies to align with the demands of intelligent education, thereby fostering a more engaging and effective learning environment (Guo, Geng, Chen, & Chen, 2022; Wang, J., & Zhan, 2021).

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