USE OF ARTIFICIAL INTELLIGENCE IN THE CONTINUOUS PROFESSIONAL TRAINING OF FUTURE SPECIALISTS IN THE SOCIONOMIC SPHERE

Abstract

Summary. Introduction. The digital transformation of higher education necessitates a shift from traditional pedagogical frameworks to dynamic, technology-driven paradigms. This need is particularly critical in the socionomic sphere (social work, psychology, counseling), where specialists face a rapidly evolving landscape of social challenges alongside an influx of AI tools in daily practice. Future professionals must acquire specialized digital competencies while maintaining their fundamental human-centered focus. However, systemic models that bridge generative AI tools with continuous professional training remain underdeveloped in current academic literature.

Purpose. The purpose of the article is theoretical justification, development and scientific substantiation of the use of artificial intelligence in the system of continuous professional development for future specialists in the field of socionomics, ensuring the development of their digital competences and readiness for lifelong learning without losing sight of the human-centred focus of their future work.

Methods. To achieve the research goals, a comprehensive set of complementary methods was applied:

– Theoretical methods: systemic, comparative-pedagogical, and structural-functional analysis of academic literature and regulatory frameworks (2023–2026) to clarify key concepts and classify AI tools by their didactic functions;

– Empirical methods: pedagogical observation and content analysis of student performance within simulated crisis-management environments.

Originality. The scientific novelty of the research lies in the development of a holistic pedagogical model that approaches artificial intelligence not merely as an isolated technical aid for routine tasks, but as a cross-cutting, continuous simulation environment. The study bridges advanced technological integration (predictive analytics and generative simulators) with strict ethical and data-protection protocols specifically tailored to the sensitive landscape of Ukrainian socionomic and post-crisis social work.

Results. The study demonstrates that integrating AI platforms into higher education shifts the instructional focus toward highly personalized learning paths, allowing systems to automatically adapt materials to individual student paces and learning gaps. Practical applications within psychology and social work curricula include the deployment of generative chat-bots acting as virtual clients to safely simulate acute crisis scenarios. Additionally, predictive data analytics tools help students master digital diagnostics and track their own competence development. Concurrently, this technological integration redefines the educator's role, shifting it from a traditional information transmitter to a facilitator and mentor focused on developing students' critical thinking and verification skills. Systemic challenges were identified, emphasizing ethical considerations, data confidentiality risks, and the necessity of aligning local models with the European Commission's ethical guidelines for trustworthy AI.

Conclusion. Artificial intelligence serves as a powerful catalyst for ensuring genuine lifelong learning and continuity in professional training. Its structured integration enhances core competencies, practical orientation, and academic motivation by shifting focus from reproductive learning to advanced prognostic and analytical thinking. Future research should prioritize building robust ethical and legal frameworks to safeguard client data and developing optimized blended-learning models where AI enhances pedagogical efficiency while fully preserving the core values of human empathy and social humanism.