ALGORITHM FOR PREPARING HIGHER EDUCATION STUDENTS TO APPLY ARTIFICIAL INTELLIGENCE IN SCIENTIFIC RESEARCH

Abstract

Abstract: This article reveals the urgency of pedagogical preparation of higher education students for the conscious, ethical, and effective application of artificial intelligence (AI) in scientific research. In the context of digital transformation in education, AI is viewed not only as a tool for automating routine tasks but also as a potential catalyst for developing students’ research autonomy, critical thinking, and academic culture. The need for a step-by-step pedagogical algorithm is substantiated, enabling systematic integration of AI tools into the training of young researchers while maintaining a balance between technical capabilities and ethical guidelines of scientific inquiry.

A review of literature, EU strategic documents, and empirical research results indicates insufficient methodological regulation of AI use in higher education. Key contradictions have been identified: between the availability of AI tools and the lack of skills for their conscious application; between AI’s potential for analytics and the weak formation of critical and research competencies; and between the active use of generative AI and the threat to academic integrity. The article emphasizes that overcoming these contradictions is possible only through the introduction of pedagogically motivated approaches to education.

Based on theoretical analysis and the testing of educational practices in higher education, an algorithm for preparing students for scientific research involving AI was developed, which includes four consecutive stages: propaedeutic, normative-ethical, practice-oriented, and project-research. Each stage is aimed at the gradual formation of academic literacy, digital ethics, critical data handling skills, and students’ research autonomy. The algorithm is grounded in principles of andragogy, ethical responsibility, practical feasibility, and interdisciplinary integration.

The practical significance of the proposed algorithm is confirmed by results of its implementation in the educational process, particularly within the educational component "Methodology and Technology of Organizing Pedagogical Research. Educational Measurements" at the Faculty of Pedagogy, Kherson State University. The article also provides a SWOT analysis of the developed algorithm, highlighting its strengths (stepwise approach, ethical orientation, practical focus), weaknesses (dependence on staff qualifications and technical resources), opportunities (adaptability to other educational levels, increased graduate competitiveness), and threats (rapid technological changes, risks of AI misuse). This analysis allows assessment of the algorithm’s prospects for scaling and further improvement of educational practices.