Integration of artificial intelligence into engineering education: analysis and prospects
DOI:
https://doi.org/10.37142/2076-2151/2025-1(54)234Keywords:
artificial intelligence, engineering education, innovative teaching methods, innovative learning methods, personalized learning, adaptive systems, intelligent tutoring systems, generative AI, analysis, prospects.Abstract
Podlesny S., Ieromin M. Integration of artificial intelligence into engineering education: analysis and prospects
This article presents a review of recent scientific publications devoted to a comprehensive study of the integration of artificial intelligence (AI) into engineering education. Innovative teaching and learning methods that actively use the potential of AI are considered in detail, such as the creation of personalized learning trajectories, the use of intelligent tutoring systems for adaptive interaction, the use of simulations and virtual environments for practical experience, the implementation of gamification to increase engagement, and automated assessment for effective feedback. Innovative teaching methods that have been significantly improved by the integration of AI are thoroughly analyzed, including the development of self-directed and autonomous learning, the expansion of collaborative learning opportunities, the use of interactive simulations and virtual reality for deep immersion, the provision of personalized feedback and guidance, as well as integration into the learning process to develop key 21st century skills. This paper provides an in-depth analysis of the current state of AI integration in engineering education, covering the levels of actual implementation of various AI tools, the main focus on specific cutting-edge technologies, the diverse applications of AI in the educational process, and the perceptions of both teachers and students regarding the benefits and potential challenges. The prospects and forecasts for the future development of AI in engineering education are covered in detail, including the expected spread of hybrid learning models, further personalization of educational programs, growing attention to the ethical aspects and responsible use of AI, and the need to develop new competencies in teachers and students. Key trends and promising directions for further research in this area are identified, and the main challenges and potential barriers that may arise on the path to successful integration of AI in engineering education are discussed in detail.
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