ARTIFICIAL INTELLIGENCE AS A DRIVER OF THE TRANSFORMATION OF TEACHING AND ASSESSMENT METHODS IN THE TRAINING OF FUTURE ENGINEERING SPECIALISTS

Abstract

The article substantiates and analyzes the transformation of teaching and assessment methods in the training of future engineering specialists under the influence of artificial intelligence technologies. The relevance of the study stems from the ongoing digitalization of the educational environment, the modernization of the professional training of future engineers, and the need to adapt the educational process to contemporary technological developments. The paper emphasizes that the integration of intelligent digital systems into professionally oriented instruction contributes to the modernization of traditional teaching methods, enhances the personalization of the educational process, and ensures continuous monitoring of students’ learning outcomes. The scientific foundations for the use of artificial intelligence in higher technical education are analyzed, and the main directions of its application in the professional training of future engineers are outlined. The analysis shows that the use of digital platforms, virtual simulators, expert systems, and intelligent applications creates favorable conditions for the formation of subjectspecific competencies, the development of analytical thinking, digital literacy, and students’ professional autonomy. The article examines the potential of artificial intelligence tools and specialized digital technologies, including ChatGPT, GitHub Copilot, MATLAB, ANSYS, IBM Watson, and TensorFlow, for developing the professional and communicative competencies of future engineering specialists. It has been established that the integration of intelligent technologies promotes practice-oriented learning, develops professional decision-making and critical analysis skills, and fosters a responsible attitude toward the use of digital systems. Particular attention is paid to the ethical aspects of the application of artificial intelligence in engineering education, including algorithm transparency, personal data protection, academic integrity, and responsibility for the consequences of using digital technologies. The findings indicate that the effectiveness of using artificial intelligence in the professional training of future engineers largely depends on the digital competence of teachers and students, the appropriate integration of innovative technologies into the educational environment, and a balanced combination of technical and humanities components of professional education.