We explore a design vision that reimagines human connection not only across distance but also across time, extending even beyond physical lifespans. While Telepresence was developed to connect remote spaces, this panel shifts the focus to remote time—the desire to be remembered, to reconnect with the past, and to speculate on possible futures. We bring together perspectives from generative AI, cultural heritage, and HCI to examine how connection across time can be mediated and reinterpreted. AI researchers develop synthetic agents "ghost bots" that reconstruct identities from fragmented data. Scholars in archaeological heritage investigate how artifacts carry memory across centuries, offering insight into how cultural traces become embodied knowledge. HCI researchers study interactive systems that honor the presence of absence. Together, this inter-disciplinary dialogue across digital reconstruction, archaeological preservation, and practices of cherishing material traces raises broader philosophical questions about how emerging technologies shape relationships across past, present, and future.
This study examines how amplifying real-time heartbeat feedback affects emotion regulation. Accurate heartbeat perception—a key facet of cardiac interoception—has been linked to emotional awareness and mental well-being, yet the causal role of interoceptive feedback in emotion regulation remains underexplored. We empirically tested whether making heart rate signals more perceptible through wearable haptic feedback could facilitate implicit emotion regulation during emotionally evocative experiences. Using a custom Fitbit-based system, thirty participants received real-time, sham, or no heartbeat-synchronized vibrations while viewing fear- and amusement-inducing film clips. Interoceptive accuracy, emotional disturbance, and the linguistic complexity of emotion descriptions were measured. Exploratory analyses showed that real-time feedback reduced emotional disturbance during fear stimuli, especially among individuals attentive to bodily sensations, though effects did not remain significant after multiple comparisons correction. Feedback primarily modulated arousal rather than valence and did not significantly affect heartbeat counting or linguistic complexity. As one of the first causal, empirical investigations of interoceptive feedback and emotion regulation, this work identifies boundary conditions for its effectiveness and offers insights for designing personalized, interoception-aware wearable technologies.
Background Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions. Objective We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. Methods We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user’s self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers. Results We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=–5.49, P<.001, Cohen d=0.36). Conclusions Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.
