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Introduction Contemporary technological interventions in the pedagogical system have created substantial learning opportunities across educational communities. Learning theories provide a framework for effective learning practices. This study focuses on pedagogical synthesis frameworks that integrate generative artificial intelligence technologies and align with learning theories to enhance learning. Methods This study employed bibliometric and content analyses to examine research articles relating to the integration of generative AI technologies, guided by learning theories, within the educational system. A total of 614 articles were collected from the Scopus and Web of Science databases using the keywords “generative artificial intelligence” AND “education” AND “digital learning”. The data were visualised and interpreted through bibliometric analysis using the Biblioshiny application integrated with RStudio. For the content analysis, 163 articles were screened through a systematic review process. Results The findings reveal influential countries, authors, and core sources in the field, as well as students' learning with artificial intelligence as an emerging theme, highlighting the prominence of generative AI in education. The penetration of generative artificial intelligence technology was higher at the higher education level (57.05%), followed by the professional level (36.80%) and the school level (6.13%). Discussion Thematic categorisation of generative AI research into adoption, academic learning, English language learning, ethical usage, and professional contexts reveals patterns in the adoption of generative AI and its determinants in the education domain. The findings also indicate a strong intrinsic link between generative AI-integrated learning approaches and the alignment of Constructivist, Connectivist, and Cognitive learning theories, particularly at the higher education and professional levels.
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P et al. (2026) studied this question.
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