<![CDATA[Artificial intelligence (AI) has moved from a long-standing research area in educational technology to a highly visible component of contemporary digital learning. This article reviews selected literature to examine the opportunities, challenges, and implications of AI integration for learning personalization, learning analytics, student experience, and digital learning equity. The review synthesizes a foundational conceptual paper, a systematic review, an experimental adaptive-learning study, a machine-learning forecasting study, and a 2025 student survey. The evidence indicates that AI can improve personalization, learning efficiency, formative feedback, and engagement when systems respond to both cognitive and affective conditions. An adaptive mathematics learning system, for example, produced better learning outcomes and lower mathematical anxiety when affective factors were considered alongside cognitive status. Learning analytics also demonstrated the potential to forecast computer-mediated communication competence from behavioral features. However, the literature highlights persistent concerns regarding accuracy, privacy, critical-thinking erosion, over-dependence, uneven model performance, and gaps between technical development and educational theory. The article argues that AI should therefore be integrated as an educational support mechanism rather than treated as a substitute for pedagogical judgment. From a research-team perspective, future work should connect mathematical and statistical modeling with educational theory, transparent evaluation, and equity-oriented governance.]]>
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