Human & Artificial Intelligence in action

This dissertation is positioned within the field of human-AI interaction, focusing on AI’s impact on workplace dynamics. The primary objective is to explore the multifaceted relationship between humans and AI, while the secondary goal is to understand of how varying degrees of AI involvement affect workers. The first objective is addressed through a systematic and quantitative review of existing human-AI literature, employing a descriptive bibliometric analysis. This involves the use of techniques such as bibliographic coupling and keyword co-occurrence to objectively map the current state of the field and identify future research directions. A significant contribution of this study is its interdisciplinary approach to examining human-AI interaction, marking the first attempt to investigate this domain from such a perspective. For the second research objective, the focus shifts to an experimental study that examines how varying levels of AI involvement in task execution influence workers’ sense of personal control. Participants were assigned a bin packing task, both with and without AI assistance, across four conditions: no AI support, low AI support, medium AI support, and high AI support. The findings reveal a significant reduction in personal control at the highest level of AI involvement, offering valuable insights into the nuanced impacts of AI augmentation on workers. This study underscores the importance of balancing AI support to maintain workers’ sense of personal control and suggests strategies for optimal AI integration that preserve employees’ engagement. From an academic standpoint, the study traces the evolution of interdisciplinary research on human-AI interaction and identifies underdeveloped areas, laying a foundation for future scholarly contributions. From a practical perspective, it provides insights for organizations and managers on effectively integrating AI systems into the workplace, enhancing their understanding of human-AI interplay, and offering recommendations for successful AI integration. This dissertation not only aims to fill gaps in the current literature but also aspires to inspire further research that deepens our understanding of human-AI interactions, ultimately aiding organizations in leveraging AI’s potential while mitigating associated challenges and risks.