The Psychology of Human–AI Interaction: Trust, Acceptance, Decision-Making and Emotional Responses

Abstract

The rapid development of Artificial Intelligence (AI) is transforming the relationship between humans and technology. AI systems are increasingly integrated into healthcare, education, finance, workplaces, transportation, communication, entertainment, and everyday decision-making. As these systems become more capable of interacting with people through natural language, visual interfaces, recommendation mechanisms, and adaptive technologies, understanding the psychological dimensions of human–AI interaction has become an important area of human-science research. This paper examines the psychology of human–AI interaction with particular emphasis on trust, technology acceptance, decision-making, and emotional responses. It explores how perceived intelligence, reliability, transparency, anthropomorphism, familiarity, usefulness, perceived risk, and previous experience influence people's willingness to use and rely on AI systems. The paper further examines the psychological mechanisms underlying appropriate and inappropriate trust in AI, including automation bias and algorithm appreciation. In decision-making contexts, AI can enhance human performance by processing large quantities of information, but excessive reliance on automated recommendations can create new risks. The emotional dimension of human–AI interaction is also considered, including anthropomorphism, emotional attachment, perceived empathy, social presence, and user responses to conversational AI and social robots. The paper argues that trust in AI should not be understood as a simple positive attitude but as a dynamic psychological relationship that should be appropriately calibrated according to system capability, uncertainty, context, and consequences. The study also emphasizes that acceptance of AI depends not only on technological performance but on users' perceptions of usefulness, ease of use, fairness, control, transparency, and social compatibility. Future research should focus on explainable AI, adaptive human–AI collaboration, culturally sensitive AI design, emotional AI, trust calibration, and long-term psychological effects. A human-centered approach is essential for developing AI systems that complement human capabilities while preserving autonomy, critical thinking, and meaningful human relationships.