This article analyzes the current state, practical applications, and promising development directions of artificial intelligence (AI) and machine learning (ML) technologies in mechanical engineering. The study examines the application of AI and ML methods in predictive maintenance, design optimization, manufacturing and process optimization, robotics and automation, quality control, and structural health monitoring. The capabilities of modern approaches, including neural networks, deep learning, reinforcement learning, transfer learning, and Bayesian optimization, in solving complex engineering problems are also discussed. Particular attention is paid to the integration of data-driven models with physics-based models, uncertainty quantification, model interpretability, reliability, and adaptability under different operating conditions. The analysis demonstrates that the integration of AI and ML into mechanical engineering provides significant opportunities for improving production efficiency, reducing maintenance costs, enhancing product quality, optimizing engineering processes, and developing sustainable engineering solutions. The study also identifies promising research directions related to physics-informed artificial intelligence, explainable AI, uncertainty-aware models, hybrid intelligent systems, and adaptive engineering technologies.
This article analyzes the current state, practical applications, and promising development directions of artificial intelligence (AI) and machine learning (ML) technologies in mechanical engineering. The study examines the application of AI and ML methods in predictive maintenance, design optimization, manufacturing and process optimization, robotics and automation, quality control, and structural health monitoring. The capabilities of modern approaches, including neural networks, deep learning, reinforcement learning, transfer learning, and Bayesian optimization, in solving complex engineering problems are also discussed. Particular attention is paid to the integration of data-driven models with physics-based models, uncertainty quantification, model interpretability, reliability, and adaptability under different operating conditions. The analysis demonstrates that the integration of AI and ML into mechanical engineering provides significant opportunities for improving production efficiency, reducing maintenance costs, enhancing product quality, optimizing engineering processes, and developing sustainable engineering solutions. The study also identifies promising research directions related to physics-informed artificial intelligence, explainable AI, uncertainty-aware models, hybrid intelligent systems, and adaptive engineering technologies.
The article examines the methodologically grounded integration of generative artificial intelligence technologies into the teaching of Arabic as a foreign language in higher education. The author proposes a model of teacher-mediated three-stage AI mediation, in which interaction with a language model comprises a teacher-designed prompt, the student’s independent dialogue with the model, and a subsequent verification and reflection stage. The model was piloted during the first semester of the 2025/2026 academic year with 55 second-year students. The results demonstrate significantly higher gains in lexical range, oral fluency and learner autonomy in the experimental group. The difference in grammatical accuracy proved less pronounced, which is explained by the language models’ errors in vocalisation and in assigning i‘rab. Methodological recommendations are offered on this basis.
