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AIGC时代人机互动理论的回顾与进展
A Review and Progress of Human-Computer Interaction Theory in the AIGC Era

DOI: 10.12677/jc.2025.135128, PP. 860-865

Keywords: 人机互动,人机传播,CASA理论,AIGC
Human-Computer Interaction
, Human-Computer Communication, CASA Theory, AIGC

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Abstract:

近年来,人工智能生成内容(AIGC)的迅速发展推动了新型人机交互模式的形成,传统的计算机中介传播分析(CASA)理论已无法适应AIGC时代的技术进步与媒介变化。本文系统梳理了人机互动和人机传播的核心理论,重点探讨了CASA理论的提出及其拓展,并对AIGC技术的特征与人机交互展开了深入分析。文章指出,尽管CASA理论为人机传播研究提供了基础,但随着技术的发展,其在解释新兴人机交互模式和跨文化情境中的局限性逐渐显现。文章还指出,目前学界从“社会线索”、“媒体代理”、“社会脚本”三条路径来扩展了CASA范式,以适应AIGC时代的需求。最后,文章展望了人机交互理论的未来研究方向。本文的研究发现为后续研究提供了理论依据,同时,本文有助于更好地理解AIGC技术对人类工作与生活的深远影响,这对于推动人机交互技术的实际应用,如决策支持、情感陪护等领域,具有重要的现实价值。
In recent years, the rapid development of Artificial Intelligence Generated Content (AIGC) has fostered the emergence of novel human-computer interaction modes, rendering traditional theories such as Computer-Mediated Communication Analysis (CASA) insufficient to accommodate the technological advancements and media transformations of the AIGC era. This paper systematically reviews core theories of human-computer interaction and communication, emphasizing the origins and developments of CASA theory, while conducting an in-depth analysis of AIGC technology’s features and their implications for human-computer interaction. It argues that although CASA theory provides a foundational framework for human-computer communication studies, its limitations have become increasingly evident in explaining new interaction patterns and cross-cultural contexts arising from technological evolution with the development of technology. The paper further notes that academia has expanded the CASA paradigm through three main approaches—“social cues”, “media agency”, and “social scripts”—to adapt it to the needs of the AIGC era. Finally, the paper discusses future research directions for human-computer interaction theory. These findings provide a theoretical foundation for subsequent research and enhance our understanding of AIGC’s profound impact on human work and life, holding significant practical implications for advancing human-computer interaction applications, including decision support and emotional companionship.

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