Researcher profile

Beata Gavurova

Beata Gavurova is a professor and interdisciplinary researcher specialising in digital transformation, artificial intelligence, data-driven decision-making, information ecosystems, and the societal impacts of emerging technologies. Her research spans AI governance and societal readiness, responsible AI adoption, digital and media literacy, disinformation and fact-checking, platform governance, algorithmic systems, digital rights, cognitive and democratic resilience, digital health, online harms, social-media behaviour, problematic digital engagement, and behavioural addictions. She also investigates organisational digital preparedness, AI-enabled decision-making, smart monitoring, automation, simulation, and technology-driven regional and industrial transformation. Her research has a strong public-policy and strategic dimension. She has served in expert groups within the competence of the Office of the Government of the Slovak Republic, the Ministry of Investments, Regional Development and Informatization of the Slovak Republic, the Ministry of Health of the Slovak Republic, and the Office of the Plenipotentiary of the Government for Roma Communities. She has contributed to the preparation of national strategic and policy documents, including the National Strategy for Regional Development of the Slovak Republic, the implementation framework related to Agenda 2030, and the Debarrierisation Strategy of the Slovak Republic. She has cooperated with public authorities and policy institutions in Slovakia and the Czech Republic, including ministries responsible for health, regional development and informatization, the National Health Information Center, and health-policy institutions. At Charles University in Prague, she has participated in research linked to projects of the Office of the Government of the Czech Republic and the Ministry of Health of the Czech Republic. This institutional engagement supports the translation of research findings into strategic documents, policy recommendations, monitoring frameworks, and evidence-based public interventions. Methodologically, her work combines large-scale representative and longitudinal surveys, comparative research, regression and multivariate modelling, structural and fuzzy models, machine learning, natural language processing, text classification, geographic analysis, composite indices, simulation, and decision-support systems, with a strong emphasis on translating empirical evidence into policy, governance frameworks, resilience tools, and applied interventions.