Babenko Dmytro, PhD (Technical Sciences), Professor, First Vice Rector, Mykolayiv National Agrarian University, Mykolayiv, Ukraine

ORCID: 0000-0003-2239-4832
e-mail: babenko@mnau.edu.ua

Dotsenko Nataliia, Doctor of Pedagogy, Professor, Professor of Department of General Technical Disciplines, Mykolayiv National Agrarian University, Mykolayiv, Ukraine

ORCID: 0000-0003-1050-8193
e-mail: dotsenkona@mnau.edu.ua

Gorbenko Olena, PhD (Technical Sciences), Associate Professor, Associate Professor of Department of Agroengineering, Mykolayiv National Agrarian University, Mykolayiv, Ukraine

ORCID: 0000-0001-6006-6931
e-mail: gorbenko_ea@mnau.edu.ua

Baranova Olena, assistant of the Department of General Technical Disciplines, Mykolayiv National Agrarian University, Mykolayiv, Ukraine

ORCID: 0000-0002-4871-8914
e-mail: baranova.ovprime@gmail.com

Integration of artificial intelligence technologies into the management system of agrotechnical education

Abstract. Introduction. Integrating AI into the learning management system enhances student engagement by providing a more interactive and personalised learning experience. In the context of teaching, it improves the efficiency of content creation, gives feedback and facilitates assessment processes. Furthermore, AI can help students to develop critical thinking and modern digital skills, both of which are essential for professionals in the field of agricultural engineering. AI can also enhance our ability to analyse complex data and model technical processes, thereby enriching students’ practical understanding and simplifying administrative processes.

Purpose. The article explores ways to integrate artificial intelligence technologies into agrotechnical education management systems.

Results. Integrating artificial intelligence tools into the educational system enables us to adapt learning to the individual needs of students and automate routine tasks, freeing up time to focus on research and innovation in the curriculum. The study of integrating artificial intelligence technologies into the management system of technical agricultural education involved comparing the effectiveness of two approaches: a combined approach, which uses a wide range of AI tools to implement the educational and management processes of higher education institutions; and a differentiated approach, in which AI technologies are divided into three groups to perform the following specialised tasks: creating visual and media content; modelling, calculations and 3D visualisation; and chatbot assistants for planning, consultation and support during the educational process. To assess this, criteria were developed for the integration of AI technologies into the management system of agricultural education. These criteria include compliance with the intended purpose, moderation of the educational process, accessibility, quality of the created content, and technical support. A statistical evaluation based on the Spearman criterion determined that preliminary differentiation of AI tools according to the expected results of their application is a more effective approach.

Conclusions. The integration of AI technologies into agrotechnical education management systems can transform the role of educators, shifting the focus from content provision to facilitating deeper learning and critical analysis. However, strengthening the mentoring and guidance of students on the ethical and effective use of AI and issues of academic integrity shuld be adressed. In the context of applying AI technologies to improve education management systems for higher education students majoring in agriculture, it is clear that teaching becomes more dynamic, effective and responsive to their individual needs.

Keywords: artificial intelligence, education management system, agricultural education, technical disciplines

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