Keywords:
higher education
integration
students' ability
future learning

  • Abstract

    The integration of artificial intelligence (AI) technologies such as ChatGPT into higher education faces complex challenges, including innovation gaps, risks of academic integrity violations, and institutional preparedness to respond to digital dynamics. The urgency of achieving quality, inclusive, and sustainable education necessitates the systematic exploration of research trends and best practices to optimize the benefits of AI while mitigating its risks. This study employs a bibliometric method, analyzing 192 Scopus-indexed articles (2023–2024) via the Biblioshiny tool to map research productivity, collaboration networks, and dominant themes. The findings indicate a rapid annual growth rate of 125.42% in publications, reflecting the accelerating scholarly interest in generative AI within academia. The results reveal the dominance of contributions from the United States, China, and Australia, which collectively shape the discourse on AI-driven pedagogical transformation, while ethical concerns such as plagiarism, algorithmic bias, and equity in access emerge as persistent barriers. The analysis further identifies three prominent thematic clusters: pedagogical innovation, ethical and institutional challenges, and technological adaptation. This study contributes to a comprehensive mapping of the ChatGPT research landscape in higher education, pinpoints critical literature gaps—particularly in educator preparedness and contextualized adoption strategies—and offers evidence-based policy recommendations. These include fostering international cooperation, developing robust ethical guidelines, and enhancing AI literacy among faculty to ensure sustainable, equitable, and pedagogically sound integration of AI technologies in higher education worldwide.

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Kurniawan, R. Y., Sugeng, B., Wahjoedi, Hermawan, A., Kamalia, P. U., & RoziqiFath, Z. F. M. (2026). Determinants of the adoption of AI technology ChatGPT in economics learning: A bibliometric analysis. Multidisciplinary Reviews, 9(11), 2026495. https://doi.org/10.31893/multirev.2026495
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