Hybrid AI enhances decision-making transparency and improves user trust in technology adoption while increasing efficiency by combining machine learning and rule-based algorithms. The aim of this research is to investigate the nature of hybrid AI systems, focusing on explainable AI and hybrid human-AI interaction in light of human-machine communication theories. In order to understand this integrated phenomenon, relevant information from 21 selected documents was collected and analyzed using an inferential content analysis approach. The findings show that hybrid interaction goes beyond simple technology adoption and affects the structure of mutual cooperation (human-machine teaming). Key parameters of this cooperation include building mutual trust through explainable AI and achieving a common understanding and awareness between humans and machines. This approach improves the quality of interaction by simulating human behaviors and creating two-way communication. Ultimately, this hybrid interaction, paves the way for the acceptance of artificial intelligence as a social actor and a member of the digital society by combining the strengths of humans and machines. This study draws a new horizon for the future of human-technology relations in social contexts by explaining the concepts of hybrid interaction and the importance of explainable artificial intelligence.