Generative artificial intelligence and Big Data in sustainable autonomous learning: a systematic review and socio-technical convergence model
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The study analyzed the impact of generative artificial intelligence and data analysis on the sustainable development of autonomous learning in digital educational contexts. A qualitative, descriptive-analytical approach was employed, based on a systematized review of recent scientific literature. The methodological process followed rigorous criteria for the search, selection, and analysis of sources, resulting in a corpus of 50 studies from high-impact academic databases.
Inductive thematic coding was applied to identify emerging patterns, trends, and relationships in the use of these technologies. The findings showed that generative artificial intelligence enhanced learning personalization, adaptive content development, and student autonomy. Likewise, data analysis supported evidence-based pedagogical decision-making, optimizing educational processes through performance evaluation and dropout prediction.
As a theoretical contribution, the Socio-Technical Convergence Model for sustainable autonomous learning was proposed, integrating technological, pedagogical, and ethical dimensions within a dynamic system. However, challenges related to data privacy, the digital divide, and algorithmic ethics were identified. It was concluded that this convergence represented a significant opportunity, provided its implementation was guided by responsible, inclusive, and ethical principles.
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