Crossing the AI Mind Trap: Deconstructing the Impact of AI Mind Perception on User Engagement in AI Voice Assistants
Qian Hu, Zhao Pan · Journal of theoretical and applied electronic commerce research · 2026
As large language models (LLMs) advance the machine mind of artificial intelligence (AI) to be more human-like, the dynamic impact of users’ perceptions of this mind on their engagement remains underexplored. Specifically, little is known about the “tipping point” where appreciation shifts to aversion. Grounded in mind perception theory, this research investigates the dynamic relationship between the perceived machine mind of AI voice assistants and user engagement using data from online reviews and experiments. Study 1 reveals a significant inverted U-shaped relationship, indicating that a moderate level of human-like machine mind maximizes user engagement, while excessive anthropomorphism triggers aversion—a form of the “uncanny valley” effect. Study 2 further dissects the differential impacts of the two core dimensions of the mind: the agentic mind (i.e., the capacity for thinking and planning) and the experiential mind (i.e., the capacity for feeling and emotion). Our findings reveal the comparative effects of AI agentic and experiential minds on the inverted U-shaped relationship, showing that a high experiential mind attenuates the positive effect of the agentic mind on user engagement. These findings deepen our understanding of mind perception effects in human–AI interaction and offer critical practical insights for AI designers on optimizing mind simulation to avoid user alienation.