Student Achievement in STEM Fields Using Deep Learning Techniques: A Review

Student achievement in Science, Technology, Engineering, and Mathematics (STEM) education has become a critical concern due to increasing academic complexity, diverse learner backgrounds, and global demands for a skilled workforce. Recent advancements in deep learning and artificial intelligence have opened new possibilities for analyzing, predicting, and enhancing student academic performance in STEM fields. This review systematically examines contemporary research on deep learning–based approaches applied to student achievement prediction, learning analytics, personalized tutoring systems, engagement analysis, and academic outcome forecasting. The study synthesizes findings from empirical studies, systematic reviews, and applied frameworks published between 2023 and 2025. Emphasis is placed on neural networks, ensemble models, NLP-supported systems, adaptive tutoring, and time-series learning analytics. The review identifies key methodological trends, performance gains, and application domains while highlighting persistent challenges such as data bias, interpretability, ethical concerns, and scalability. By consolidating current research evidence, this review provides insights into how deep learning techniques are reshaping STEM education assessment and decision-making, and it outlines future research directions for building robust, transparent, and inclusive intelligent educational systems.