METHODOLOGICAL FOUNDATIONS FOR THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN TEACHING SPECIALIZED DISCIPLINES: OPPORTUNITIES AND RISKS
TL;DR
The paper analyzes key directions of AI integration into educational practice, including adaptive learning systems, intelligent tutoring, automated assessment, and personalized learning pathways, and contributes to the development of a structured approach to integrating artificial intelligence into higher education.
Abstract
The article examines the methodological foundations of applying artificial intelligence technologies in the teaching of specialized disciplines, with a focus on identifying both their pedagogical potential and associated risks. The relevance of the study is determined by the ongoing digital transformation of education and the growing demand for innovative tools that enhance the effectiveness and adaptability of the learning process. The paper analyzes key directions of AI integration into educational practice, including adaptive learning systems, intelligent tutoring, automated assessment, and personalized learning pathways. Particular attention is given to the methodological principles that ensure the effective implementation of these technologies, such as alignment with learning objectives, didactic appropriateness, and the role of the instructor in a technology-enhanced environment. At the same time, the study highlights a range of challenges and risks related to the use of artificial intelligence, including potential bias in algorithmic decision-making, issues of academic integrity, reduced critical thinking, and increased dependence on digital tools. The importance of maintaining a balance between technological innovation and pedagogical control is emphasized. The results of the study contribute to the development of a structured approach to integrating artificial intelligence into higher education, aimed at improving the quality of teaching specialized disciplines while minimizing potential negative impacts.