Abstract
Objective: In this article, the aim is to explore in detail how AI is specifically applied to technical and technological education within universities. Methods: Data collection was conducted through a comprehensive search in academic databases, using key terms such as "virtual learning environments" and "higher education." Inclusion criteria were established to select relevant studies, excluding those focused on primary or secondary education. Qualitative data analysis identified patterns and trends in the implementation of VLEs in higher education, highlighting effective practices and common challenges. The benefits of VLEs, such as universal access to educational resources and global collaboration among researchers, were emphasized, but limitations were also acknowledged, such as limited technological infrastructure and the need for teacher training. Although the possibility of omissions and biases in the study was recognized, a conscious effort was made to minimize them through a transparent and systematic approach to data collection and analysis. Result: This strategy of building a document matrix allowed for covering a wide range of research on the implementation and effectiveness of Virtual Learning Environments (VLEs) in higher education. Each article underwent rigorous analysis to identify relevant data related to the research objectives, methodologies, results, and conclusions. The development of this matrix represents a fundamental step in the research process by facilitating a structured and systematic evaluation of the existing literature in this constantly evolving field.
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