Generative Artificial Intelligence and the future of society: a multidisciplinary analysis of economic transformation, human behavior, education, and digital governance

Abstract

Generative Artificial Intelligence (GenAI) has quickly transitioned from a niche technological innovation into a growing and powerful force that is transforming the nature of work, the workplace, education, communication, creative processes, and public decision-making. Its increasing visibility poses significant challenges to the way societies will adapt to the new economic organization, new ways of acting, learning and digital governance. This qualitative research focuses on these intertwined transformations from a multidisciplinary perspective as part of a body of work that tends to explore each of the economic, behavioral, educational, and governance aspects of GenAI separately. The study follows an interpretivist research perspective for a qualitative analysis of the documentary, content analysis of scholarly articles, and thematic synthesis of scholarly documents, policy documents, and authoritative institutional sources to uncover broader patterns in the societal development of GenAI. The analysis reveals six interrelated themes: changing characteristics of knowledge work and economic activity, shifting patterns of human cognition, creativity, communication and agency, restructuring of teaching, learning and assessment, inequalities in access and AI literacy, new concerns over privacy, bias, misinformation, IP and accountability, and the need for adaptive and human-driven digital governance. The results indicate that GenAI should not be seen as an instrument of technological advancement or a guarantee of social turmoil. Instead, its impacts on society are mediated through the ways these technologies are incorporated into pre-existing social systems by institutions, government, teachers, institutions/organisations and individuals. The study builds on interdisciplinary scholarship by linking the economic, behavioral, educational, and governance perspectives in a single qualitative framework. It calls on for more robust governance, human oversight, access, and AI literacy, as well as more ethical institutional practices, to ensure effective and responsible adoption of AI.