Autonomous driving paper index
Artificial Intelligence (AI) and Machine Learning (ML) Learning Applications
One-line summary
Artificial Intelligence (AI) and Machine Learning (ML) are transforming learning environments by enabling personalized, adaptive, and intelligent educational systems.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Artificial Intelligence (AI) and Machine Learning (ML) are transforming learning environments by enabling personalized, adaptive, and intelligent educational systems. This research paper presents a systematic review of AI and ML applications in education, focusing particularly on their integration in e-learning platforms and adaptive learning technologies. The study synthesizes findings from recent scholarly literature, highlighting how AI/ML algorithms optimize learning paths, enhance student engagement, and improve academic performance across diverse educational contexts. It discusses challenges suchas ethical concerns, data privacy, and inequality in access to AI-powered learning tools. The uneven adoption of AI in education across global regions underscores the need for strategic policy and teacher training programs to ensure equitable technology integration. Additionally, the paper addresses applications beyond education, including AI/ML in cybersecurity, manufacturing, urban design, and intelligent systems, illuminating broader trends and future opportunities. This comprehensive analysis aims to provide an inclusive framework that informs educators, technologists, and policymakers about both the potentials and limitations of AI/ML in learning applications.
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