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Visualizing the Dual Landscape of Artificial Intelligence in Higher Education: A Multimethod Systematic Review and Bibliometric Analysis

Charles Kidega 1 * ,
Aciro Can Lucy 2,
Egbosimba Chinazom Irene 1,
Fobi Solomon 1
1 Zhejiang Normal University, CHINA2 Kyambogo University, Kampala, UGANDA* Corresponding Author
Educational Point, 3(2), 2026, e165, https://doi.org/10.71176/edup/19172
Publication date: Aug 18, 2026
Review Article
Open Access Open Access
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ABSTRACT

The Higher Education sector is being revolutionized by AI, with machine learning, natural language processing and generative AI applications. Yet, with the fast pace of technological evolution, both the possibilities and threats of AI incorporation have been spread out. This study includes an integrated multimethod systematic review and bibliometric analysis of the dual landscape of AI in higher education. Following PRISMA 2020 guidelines, systematic searches of Web of Science (n=1,631), PubMed (n=1,349), and Scopus (n=1,439) yielded 4,419 records (2021-2025). Following screening for duplication and eligibility, 110 studies (85 high and 25 medium quality) were included for thematic synthesis. A concurrent bibliometric analysis of 1,227 documents in Scopus was performed to depict author citation networks, keyword co-occurrence, and bibliographic coupling by source and country, using VOSviewer. The study identified Jiao, Ouyang, and Zheng as the most cited authors; the most frequently used keywords are 'ChatGPT' (173 times) since late 2022. Academic support, automated grading, teacher development, perceived usefulness, personalization of learning, and prediction of performance. The perceived ease of use, 24/7 access to technology, administrative efficiency; and academic integrity, data issues, integration barriers, technological limitations, equity concerns. And attitudinal barriers were identified as opportunity and challenge domains, respectively, during the thematic synthesis process. This study offers a dual landscape framework which means that there are no opportunities without challenges, and no challenges without opportunities. It delivers an evidence-based typology for institutional AI strategy, and priority areas for policy intervention, practically. The methodologically, it shows the usefulness of the combination of bibliometric and systematic review for complete literature synthesis. This research aims to explore the opportunities and challenges in integrating AI into higher education, based on a systematic review and bibliometric analysis of relevant literature.

KEYWORDS

AI integration opportunities challenges systematic review bibliometric analysis

CITATION (APA)

Kidega, C., Lucy, A. C., Irene, E. C., & Solomon, F. (2026). Visualizing the Dual Landscape of Artificial Intelligence in Higher Education: A Multimethod Systematic Review and Bibliometric Analysis. Educational Point, 3(2), e165. https://doi.org/10.71176/edup/19172
Harvard
Kidega, C., Lucy, A. C., Irene, E. C., and Solomon, F. (2026). Visualizing the Dual Landscape of Artificial Intelligence in Higher Education: A Multimethod Systematic Review and Bibliometric Analysis. Educational Point, 3(2), e165. https://doi.org/10.71176/edup/19172
Vancouver
Kidega C, Lucy AC, Irene EC, Solomon F. Visualizing the Dual Landscape of Artificial Intelligence in Higher Education: A Multimethod Systematic Review and Bibliometric Analysis. Educational Point. 2026;3(2):e165. https://doi.org/10.71176/edup/19172
AMA
Kidega C, Lucy AC, Irene EC, Solomon F. Visualizing the Dual Landscape of Artificial Intelligence in Higher Education: A Multimethod Systematic Review and Bibliometric Analysis. Educational Point. 2026;3(2), e165. https://doi.org/10.71176/edup/19172
Chicago
Kidega, Charles, Aciro Can Lucy, Egbosimba Chinazom Irene, and Fobi Solomon. "Visualizing the Dual Landscape of Artificial Intelligence in Higher Education: A Multimethod Systematic Review and Bibliometric Analysis". Educational Point 2026 3 no. 2 (2026): e165. https://doi.org/10.71176/edup/19172
MLA
Kidega, Charles et al. "Visualizing the Dual Landscape of Artificial Intelligence in Higher Education: A Multimethod Systematic Review and Bibliometric Analysis". Educational Point, vol. 3, no. 2, 2026, e165. https://doi.org/10.71176/edup/19172

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