- Бруяка, А.В. (orcid.org/0009-0007-3826-2988), Коваленко, В.В. (orcid.org/0000-0002-4681-5606), Мар'єнко, Майя Володимирівна (orcid.org/0000-0002-8087-962X), Семеріков, С.О. (orcid.org/0000-0003-0789-0272) and Шишкіна, М.П. (orcid.org/0000-0001-5569-2700) (2025) Prospects of using generative artificial intelligence to support the educational activities of students of higher education institutions Освіта та розвиток обдарованої особистості, 4 (99). pp. 55-62. ISSN 2309-3935
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Abstract
The article provides a comprehensive analysis of the prospects for integrating generative artificial intelligence (AI) into the educational process of higher education institutions (HEIs) to support students’ learning activities. The study addresses the urgent need for a scientifically grounded strategy that maximizes the didactic potential of AI while mitigating associated risks. The research methodology is based on a systematic review of contemporary literature, which identifies key challenges, including the lack of validated pedagogical approaches, threats to academic integrity, and ethical concerns related to data privacy and algorithmic bias. Based on this analysis, the paper proposes a functional classification of generative AI services, grouping them into four categories: 1) large language models and chatbots (e.g., ChatGPT, Perplexity AI); 2) tools for visual content generation (e.g., Midjourney, DALL·E 3); 3) specialized academic assistants (e.g., Scite, Elicit); 4) AI-powered programming tools (e.g., GitHub Copilot). This classification serves as a practical framework to help educators and students select appropriate tools for specific educational objectives. A key contribution of the study is the development of a detailed set of criteria for the effective and safe use of generative AI. These criteria are divided into two groups: effectiveness criteria, including relevance to learning tasks, accuracy and reliability of information, depth of analysis, and pedagogical appropriateness; and safety criteria, encompassing data confidentiality, academic integrity, source transparency, and awareness of potential biases. The article systematically examines the pedagogical benefits and risks of generative AI. Identified advantages include enhanced personalization of learning trajectories, increased student engagement through interactive tools, significant time savings in routine tasks, and the development of essential digital literacy skills. At the same time, major risks include a potential decline in students’ critical thinking, increased academic dishonesty, the spread of misinformation due to AI-generated hallucinations, and the widening of digital inequality. To address these challenges, the paper formulates practical recommendations for three stakeholder groups. For students, it emphasizes using AI as a tool for idea generation, critically verifying all AI-produced content, developing prompt-engineering skills, and maintaining transparency in academic work. For educators, recommendations include integrating AI into learning tasks, shifting assessment emphasis from final products to research processes, and fostering information hygiene. For HEI administrators, the study highlights the importance of developing institutional policies, providing professional development for faculty, and promoting international collaboration to exchange best practices. The study concludes that the strategic integration of generative AI represents not merely a technological enhancement but a fundamental shift toward a more personalized, efficient, and interactive educational paradigm. The effectiveness of this shift depends not on restricting AI-technologies, but on their thoughtful, ethical, and pedagogically sound incorporation into academic culture, thereby equipping students with future-ready competencies.
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