From Text to Self: Users' Perception of AIMC Tools on Interpersonal Communication and Self

In the rapidly evolving landscape of AI-mediated communication (AIMC), tools powered by Large Language Models (LLMs) are becoming integral to interpersonal communication. Employing a mixed-methods approach, we conducted a one-week diary and interview study to explore users' perceptions of these tools' ability to: 1) support interpersonal communication in the short-term, and 2) lead to potential long-term eects. Our ndings indicate that participants view AIMC support favorably, citing benets such as increased communication condence, nding precise language to express their thoughts, and navigating linguistic and cultural barriers. However, our ndings also show current limitations of AIMC tools, including verbosity, unnatural responses, and excessive emotional intensity. These shortcomings are further exacerbated by user concerns about inauthenticity and potential overreliance on the technology. We identify four key communication spaces delineated by communication stakes (high or low) and relationship dynamics (formal or informal) that dierentially predict users' attitudes toward AIMC tools. Specically, participants report that these tools are more suitable for communicating in formal relationships than informal ones and more benecial in high-stakes than low-stakes communication.

• Human-centered computing ! Empirical studies in HCI.