The Mining Industry has changed significantly in recent decades with the introduction of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the Mining Industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the Mining Industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.

The article deals with the problem of objective knowledge assessment using expert automated systems for monitoring and evaluating knowledge. The expert systems of control and knowledge estimation are usually developed on the basis of different approaches to constructing a question and answer on test. The analysis of existing testing methods made it possible to conclude that questions with selective and selectively constructed types of answer do not always allow objectively assessing the knowledge of trainees, which reduces the stimulating effect of pedagogical evaluation on cognitive activity of trainees, and the quality of the learning process as a whole. The article proposes a method of knowledge estimation, which is based on a new approach to constructing a question and answer that allows a freely designed form of a test response. Such answer is analyzed by a set of criteria for its formation based on the developed algorithms for the criteria for assessing their quality. This makes it possible to apply the obtained results to solve the scientific problem of objective knowledge assessment using testing. The proposed approach gives an opportunity to expand the functions of tests, thereby increasing the approximation degree of the estimation for the test to the level of the real knowledge of students. The purpose of the article is to justify the effectiveness and develop the expert system of control and knowledge assessment. The article is addressed to a wide range of researchers and experts in the field of education and information technologies.

Introduction. The article deals with the problem of objective knowledge assessment using expert automated systems for monitoring and evaluating knowledge. The relevance of this study is determined by qualitatively improving the monitoring and evaluation of knowledge using a testing method. The purpose of the work was to develop a set of criteria for analysing a test response along with an objective approach to their assessment. Materials and Methods. The study was conducted on the basis of the system analysis and B. Bloom’s method tracking learning outcomes, as well as the method widely used in the study of the quality of studies on the assessment of educational achievement of students: Program for International Student Assessment. In order to study the problem, 102 teachers and 357 students of two universities – Karaganda State Technical University and Karaganda State University named after E.A. Buketov – in Karaganda, Kazakhstan were interviewed and their responses observed. Results. The results of the work are of interest to the scientific and pedagogical community, as well as practicing teachers. The article proposes a new approach to constructing a test question to allow a response in a freely-constructed form. Such an answer can be analysed by a set of criteria for its formation based on the developed algorithms for a criterial assessment of their quality. This makes it possible to apply the obtained results to solve the scientific problem of objective k nowledge assessment using testing. Discussion and Conclusion. The completed study has expanded the representation of the possibilities of using the expert systems for monitoring and evaluating knowledge and increasing the objectivity of knowledge acquired during learning processes. The received results represent the practical importance for teachers involved in the development of expert control systems and assessment of knowledge. The prospect of further study consists in improving the testing method by expanding the set of criteria for assessing knowledge.

Ensuring the stability of surrounding rock in underground excavations is a critical prerequisite for safe mining operations. This study examines the mechanisms of wedge failure formation and determines the performance of a combined support system (rock bolts + shotcrete) through probabilistic analysis. Field investigations in the Zhylandy ore field (Kazakhstan) included fracture mapping, rock mass quality assessment (RQD), fracture frequency (FF), and in situ stress measurements, which confirmed a thrust-faulting regime. Numerical modeling with Dips ver.8 and UnWedge ver.6 software (Rocscience) identified critical excavation orientations of 120° and 141° associated with maximum-volume wedge formation, as well as a “safe orientation window” of 70° ± 10°. The probabilistic analysis showed that rock bolts alone yield a factor of safety (FS) < 1.2, whereas the combined support system increases FS to 2.4–3.5, significantly reducing the likelihood of wedge failures. An adaptive framework integrating numerical modeling with intelligent monitoring (“monitor → update model → adjust support”) is proposed, allowing real-time adjustment of support parameters and optimization of material consumption. The practical significance of this work lies in providing design-ready recommendations for support selection and excavation orientation, contributing to accident prevention and sustainable mining operations.

The mining industry has changed significantly in recent decades with the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the mining industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the mining industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.

The article discusses the issues of training STEM specialists in the implementation of joint educational programs (JEP) of medical and technical universities in the field of information technology in healthcare. A model of JEP training is proposed through the collaboration of professional standards, learning outcomes, requirements for specialists imposed by production partners, which allow to increase the efficiency of the do[1]mestic education system.