Machine Behavior: Paradigm Shift of Communication Research in AI Era

The widespread integration of artificial intelligence (AI)–driven communication agents, particularly large language models, has brought about a paradigm shift in communication research. Traditional human-centered paradigms are becoming inadequate for analyzing the behaviors and impacts of these non-human agents, which exhibit autonomous and intelligent communicative capabilities. This article advocates for the development of a Machine Behavior Paradigm in communication studies. This proposed paradigm explicitly expands the disciplinary scope by integrating machine behavior into three core dimensions. About research objects, it emphasizes the shifts from a human-only focus to a dual focus on humans and intelligent machines as communicative subjects. For research approaches, it should expand beyond human–human relationships to encompass machine–machine and hybrid human–machine interactions. In methodologies, we advocate to integrate computational indicators, such as text-generation temperature and activity frequency, alongside traditional empirical tools to address the opacity of AI “black boxes.” Furthermore, the article examines the limitations of current communication theories, such as agenda-setting and the spiral of silence, when applied to AI-driven environments. Also, we argue that it is difficult to study opaque AI systems with traditional methodologies. This shift necessitates interdisciplinary collaboration and the establishment of robust ethical guidelines to ensure the responsible study of autonomous agents. By outlining a systematic framework for individual, collective, and hybrid machine behavior, this article provides a roadmap for future inquiry.