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arXiv · 2609.14639

Understanding the Design Taxonomy of AI-Mediated Interpersonal Communication Experiences in HCI: A Scoping Analysis

Chen Chen·Lingyao Li·Renkai Ma·Rawan Alghofaili·Shaoze Zhou·Bojun Zhang·Xian Su·Weidong Zhu·Christine Lisetti·Mo Sha
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Abstract

Interpersonal communication is a fundamental aspect of everyday life, shaping interactions across workplaces, education, entertainment, healthcare, and beyond. While computer-mediated communication has been extensively studied, a comprehensive understanding of AI-Mediated Interpersonal Communication (AIMIC) remains lacking. An in-depth scoping analysis is urgently needed to understand the research landscape of AIMIC in HCI, particularly following the recent growth of large foundation models, and AI agent research. We conducted a scoping analysis to understand AIMIC by performing an in-depth review of prior HCI literature published over the past decade (January, 2016 - May, 2026). Grounded in the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach, we curated 52 full-paper publications from the HCI literature spanning a range of interpersonal communication contexts. We analyzed this corpus by examining the types of AIMIC studied, AI integration approaches and human-AI interaction design, reported outcomes and benefits, and key challenges and future research opportunities.

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Chen Chen, Lingyao Li, Renkai Ma, Rawan Alghofaili, Shaoze Zhou, Bojun Zhang, Xian Su, Weidong Zhu, Christine Lisetti, Mo Sha. 2026-09-13. Understanding the Design Taxonomy of AI-Mediated Interpersonal Communication Experiences in HCI: A Scoping Analysis. https://arxiv.org/abs/2609.14639

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