Perceived Emotional Adaptation of ChatGPT and Its Psychological Impact on Human Users A Mixed-Methods Study

Abstract

The objective of this study was to investigate the emotional bond between humans and ChatGPT whereby a close examination of the
components such as empathy, adaptation and trust were conducted during human–AI interaction. The quantitative and qualitative data were collected from 200 adults aged between 18 to 30 years who frequently use ChatGPT using a mixed-methods correlational
design. In the quantitative phase, a survey was conducted to analyze the relationship between perceived emotional adaptation
and users’ trust and attachment toward AI. The qualitative phase consisted of interviews to explore what participants subjectively feel
during the interaction. The results of the study showed that emotional adaptation perception considerably correlated with trust
toward ChatGPT (r = .48, p .01). The results of a regression analysis showed that emotional adaptation was a significant predictor of trust and attachment, explaining 23% of the variance (β = .48, p < .001). Thematic analysis of qualitative data produced four prominent
themes: perceived emotional memory; temporary emotional comfort; rational empathy versus real emotion; and trust and caution.
Participants notices that ChatGPT gave empathetic and calming responses which created comforting emotional feelings and
psychological relief despite users being aware of its machine nature. The combination of the two data sets suggests that ChatGPT’s
emotionally adaptive responses encourage trust, attachment and short-term emotional well-being despite having no consciousness or
long-term emotional memory. This new knowledge enhances our understanding of human–AI emotional interaction. It shows that
perceived empathy, not actual emotion, drives psychological engagement with AI. The research has practical implications for the
design of emotionally supportive AI systems in the education, therapy, and mental health arenas, while also calling ethical attention to the need for transparency in AI–human communication.