Mapping the Landscape of AI-Enhanced Collaborative Learning: Evolution and Constraints
DOI:
https://doi.org/10.70211/sakaguru.v2i2.280Keywords:
AI-enhanced Collaborative Learning; Bibliometric Analysis; Educational Data Mining; Machine Learning; Pedagogical InnovationAbstract
This study maps the scientific evolution of AI-enhanced collaborative learning over the past decade (2014–2024) through a comprehensive bibliometric analysis using the Bibliometrix R-package. A total of 660 documents retrieved from the Scopus database were analyzed to identify publication trends, key authors, thematic clusters, and research collaborations. Results indicate exponential growth since 2021, coinciding with the democratization of generative and machine-learning technologies. The field is marked by strong interdisciplinarity—bridging computer science, education, and engineering—and by geographic concentration in the United States and China. Thematic mapping reveals three consolidated lines of inquiry: technological innovation, pedagogical applications, and ethical-social challenges, with a clear transition toward advanced topics such as federated and adversarial learning. Despite this progress, regional inequality and limited empirical validation persist. The study contributes a structured overview of the field, highlighting both conceptual consolidation and critical gaps, and proposes future directions for developing inclusive, context-aware, and ethically responsible frameworks for AI-mediated collaborative education.
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