APPLICATION OF ARTIFICIAL INTELLIGENCE IN ASSESSING THE SOCIAL IMPACT OF RESEARCH PROJECTS
Keywords:
artificial intelligence, social impact, research projectsAbstract
This article aims to conduct an analytical review of the scientific literature regarding modern approaches that use artificial intelligence (AI) to assess the social impact of research and innovation projects. The review was conducted in the indexed scientific databases Web of Science and Scopus for the period until June 2025. In methodological terms, the search protocol was developed in accordance with the recommended elements for a systematic review of the scientific literature. In the context of dynamic technological and social transformations, the assessment of the social impact of research projects has established itself as a key element in sustainable development strategies. Artificial intelligence as a modern tool offers innovative solutions for monitoring, forecasting and management, supporting informed management decisions. The application of AI in assessing the social impact of research projects combines data analysis and processing with the ethical and social dimensions of scientific activity. The results show that some of the articles use artificial intelligence to analyze social emotions, behavior and perceptions in real time, especially in the context of infrastructure and healthcare projects. Other scientific studies apply AI to improve team dynamics and engagement in educational and project environments. Artificial intelligence is also used in construction, where socio-economic impact is modeled through digital twins and decision-making solutions. There is interest in integrating AI into the processes of assessing the sustainability and social responsibility of fintech and technology companies. According to some authors, the toolkit for assessing and measuring the social impact of innovation projects is based on social indicators. Particular attention is paid to the transparency and explainability of AI-based models, as well as their local applicability in sensitive social contexts. This article is structured in three parts. The first part presents the research framework, which serves as the methodological basis of the conducted review of the scientific literature. The second part summarizes the main results, focusing on good practices. The third part includes a brief discussion, interpreting the results in the context of the study objectives.
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