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<p>Given the growing role of artificial intelligence (AI) in research, most scholars have focused on how AI can facilitate the research process. While these reflections are valuable broadly, it is equally important to consider discipline-specific implications. This reflective essay provides a first perspective on the use of generative AI in communication science, a field uniquely concerned with dynamic communication phenomena, a complex interplay between humans and technologies, and context-dependency of responses. Focusing on the key method within this field i.e., experimental research, we discuss how generative AI can generate a wide variety of stimuli that are internally and externally valid in a short amount of time and simulate large numbers of responses to experimental designs. However, key questions remain regarding the extent to which AI can capture the complexity of this field. Across this discussion, we identify three paradoxes: (1) AI replaces and requires the experimenter, (2) AI simultaneously simplifies and complicates experimental research, and (3) AI generates artificial stimuli that can appear more real than reality. We conclude that systematic analyses are urgently needed to assess the specific conditions under which the power of AI can be effectively utilized to tackle methodological challenges in this field.</p>

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The end of experimental research as we know it? A perspective on generative artificial intelligence in communication science

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Jörg Matthes,
  • Sofie Vranken

摘要

Given the growing role of artificial intelligence (AI) in research, most scholars have focused on how AI can facilitate the research process. While these reflections are valuable broadly, it is equally important to consider discipline-specific implications. This reflective essay provides a first perspective on the use of generative AI in communication science, a field uniquely concerned with dynamic communication phenomena, a complex interplay between humans and technologies, and context-dependency of responses. Focusing on the key method within this field i.e., experimental research, we discuss how generative AI can generate a wide variety of stimuli that are internally and externally valid in a short amount of time and simulate large numbers of responses to experimental designs. However, key questions remain regarding the extent to which AI can capture the complexity of this field. Across this discussion, we identify three paradoxes: (1) AI replaces and requires the experimenter, (2) AI simultaneously simplifies and complicates experimental research, and (3) AI generates artificial stimuli that can appear more real than reality. We conclude that systematic analyses are urgently needed to assess the specific conditions under which the power of AI can be effectively utilized to tackle methodological challenges in this field.