METHODOLOGICAL FOUNDATIONS FOR THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE IN THE EDUCATIONAL PROCESS: CHALLENGES AND OPPORTUNITIES

Abstract

The paper examines methodological foundations for integrating generative artificial intelligence in education in Ukraine amid digital transformation. It clarifies the notions of generative AI and large language model and delineates their didactic affordances and limits. The absence of coherent institution-level risk management and unified policies for data handling, academic integrity, and responsible deployment is noted. Opportunities are mapped across four domains. In teaching, GenAI enables personalization of content and pace, rapid formative feedback, writing support, and generation of lesson plans, tasks, and rubrics. In assessment, it supports criterion-referenced rubrics, item generation, and faster feedback cycles that free time for dialogue. In administration, GenAI assists with routine automation and document flows, including drafting official templates and validating consistency of program materials. In addition, accessibility services (text-to-speech, speech recognition, image analysis, and content adaptation) expand participation for learners with diverse needs and multilingual backgrounds. Alongside benefits, the study highlights challenges: protection of personal data and privacy, algorithmic bias, model hallucinations, and the need for fact checking, risks to academic integrity, unequal access, and total cost of ownership. To address these, the article proposes a practical framework that combines clear institutional policies and procedures with transparent consent and logging, development of digital and information literacy for teachers and students including task formulation, verification of claims, and correct citation of AI interactions, a human in the loop didactic design emphasizing pedagogical appropriateness, gradual adoption, and balance with traditional methods, and evidence based monitoring using pilots, measurable outcomes, and peer review. The novelty lies in consolidating fragmented guidance into a context-sensitive roadmap connecting governance, pedagogy, and infrastructure. Practical significance includes adaptable templates for course and policy design, recommendations for professional development, and scenarios for responsible classroom use. Boundary conditions are outlined, including reliable connectivity, secure platforms that meet data protection requirements, sustained support for educators through mentoring and micro learning, and equity mechanisms that ensure meaningful access across regions.