The impact of artificial intelligence on personalized learning, student engagement, and motivation in higher education: A systematic review
Abstract
The extensive innovation of Artificial Intelligence in post-secondary education has already changed how institutions can design, implement and assess personalized education but there are still important questions around how it affects student engagement, motivation, equity and ethical practice. With the emergence of adaptive learning system and intelligent tutoring system, generative AI, learning analytics and AI-driven recommender systems rapidly becoming common in universities, a new demand exists to determine how the technologies can impact academic performance, student satisfaction and long-term learning outcomes. This is a systematic review, which uses PRISMA framework to look into the effects of using Artificial Intelligence in personalized learning, student engagement, and motivation of higher education. Keywords that were used to find relevant studies in key academic databases were: Artificial Intelligence, adaptive learning, student-centered learning, educational technology, predictive analytics, self-regulated learning, and digital learning ecosystems. The identified literature was filtered and checked with the assistance of eligibility analysis tools, followed by the extraction of primary tendencies, new uses, and unsolved issues of AI-based pedagogy. The review indicates that Artificial Intelligence can be greatly used to personalized learning to facilitate adaptive assessment, customized feedback, competency-based learning, and real-time content recommendations. AI-based applications like chatbots, virtual learning environments, machine learning algorithms, and educational data mining systems improve cognitive engagement, behavioral engagement, and emotional engagement through encouraging interactivity, flexibility, and personalized support.
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