Abstract
The recent advancements in conversational artificial intelligence have developed systems that are able to mimic human conversation, which can create the perception of hearing or understanding from a machine. This situation poses significant psychological issues about the nature of how human understanding is generated with AI, even though it doesn't have a real brain. What are the psychological mechanisms in relation to a perceived understanding when engaging in AI conversation and what are the key interactional factors that help create this experience? A conceptual mixed-methods approach was used that involved the results of human–computer interaction research, social psychology, and computational linguistics. It involved secondary empirical literature analysis and modelling of perceived understanding as a function of language alignment, contextual coherence, personalization, response timing and anthropomorphic cues. The findings suggest that high linguistic alignment, contextual coherence and adaptive personalization are important factors for perceived understanding. Faster response times and anthropomorphic design attributes contribute further toward social presence and emotional interaction. Together, these factors create a sense of empathy and understanding, leading users to attribute awareness to an algorithmic system where none exists. Based on the above findings, the study concludes that perceived understanding in AI conversations is a psychologically constructed experience that is driven by interactional design, not by machine understanding. This not only boosts user engagement and trust but also introduces the risk of emotional over-attribution and reliance on AI systems.