AI shapes new ways people communicate through technology
Understanding the Design Taxonomy of AI-Mediated Interpersonal Communication Experiences in HCI: A Scoping Analysis
Human-Computer Interaction
Summary
Communicating with others is a big part of life, and technology often helps this happen. The authors looked at 52 studies from the last 10 years to see how artificial intelligence (AI) changes and supports communication between people using computers. They found many ways AI is used to make communication easier or different, but there are also challenges to solve. This work helps us understand how AI changes our conversations when it’s involved behind the scenes.
What this means in practice
- •For software developers: Design AI communication features informed by a broad understanding of AI-mediated interpersonal interaction contexts.
- •For user experience designers: Craft user interfaces that integrate AI to support nuanced interpersonal communication based on studied approaches and challenges.
A survey. It maps existing work.
Authors
Chen Chen, Lingyao Li, Renkai Ma, Rawan Alghofaili, Shaoze Zhou, Bojun Zhang, Xian Su, Weidong Zhu, Christine Lisetti, Mo Sha
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.