Recent developments in artificial intelligence (AI) have made the long-considered idea of automated healthcare more feasible. Some researchers suggest AI can deliver more empathic responses than humans. Others have integrated it into various mental health protocols. However, given AI’s lack of inner emotional life, some have claimed it cannot deliver certain aspects of human empathy genuinely. Our previous research indicates that perceived AI involvement reduces perceived empathy, and that emotional sharing and care have unique value when perceived as human. The current study aims to explore the mechanisms behind this phenomenon, using a novel interdisciplinary approach. We will use eye-tracking to examine the focus of reading patterns for empathic responses when these are presented as AI-generated or human-authored, where in reality, the presented responses will be from both sources. This will allow us to test whether participants approach and process empathic responses differently given their perceived source, actual source, and the interaction of the two. We hypothesize that both the entire response, and emotional words specifically, will garner more focus when presented as human-authored. Given empathy’s crucial role in emotional relationships, the expected results will have important implications for AI’s proper use, especially in mental health.