Artificial Intelligence tools in university LMS: Identification, classification, and integration trends through a systematic review
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Abstract
The integration of artificial intelligence (AI) into learning management systems (LMS) is transforming higher education by enabling more personalized, interactive, and student-centered learning experiences. However, the growing diversity of available tools makes it difficult to organize them according to clearly differentiated pedagogical functions. This article aimed to identify and classify the types of artificial intelligence tools integrated into learning management systems used in higher education. A systematic literature review was conducted following the PRISMA 2020 guidelines, based on a search in Scopus and the application of previously defined inclusion and exclusion criteria. The findings identified six main functional categories: intelligent management, personalization and adaptation, intelligent tutoring and assistance, assessment and feedback, monitoring and analytics, and content generation. The most frequently reported tools included chatbots, conversational assistants, adaptive systems, predictive analytics, intelligent agents, generative AI applications, and recommendation systems. Reported benefits were mainly associated with personalization, immediate feedback, learner autonomy, engagement, and continuous academic support, while recurring limitations involved privacy, bias, accuracy, infrastructure, and technological dependence. The study concludes that AI integration into LMS should be guided primarily by pedagogical purposes rather than by technological availability alone.
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