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Understanding group communication dynamics is essential in fields such as psychotherapy, education, and organizational development. This paper introduces a novel method for automatically analyzing verbal interactions in group settings using large language models (LLMs). Drawing on Foulkes’ theory of group analysis, we define six semantic dimensions of communication and apply multivariate scoring to utterances. The method is validated on a real therapeutic session. By aggregating these scores over time and extracting dynamic indicators we characterize group development and compare model outputs to expert human assessments. Results show that while GPT-4o and Gemini Flash 2.0 demonstrate reasonable agreement with human ratings, they differ in temporal sensitivity and responsiveness. The study underscores the potential of LLMs in supporting group dynamics analysis.

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Automatic Assessment of Verbal Communication Components in Group Processes

  • 会议论文

  • 发表日期：

    2026年

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  • Marcin Jodłowiec,
  • Julia Piecuch-Jodłowiec

摘要

Understanding group communication dynamics is essential in fields such as psychotherapy, education, and organizational development. This paper introduces a novel method for automatically analyzing verbal interactions in group settings using large language models (LLMs). Drawing on Foulkes’ theory of group analysis, we define six semantic dimensions of communication and apply multivariate scoring to utterances. The method is validated on a real therapeutic session. By aggregating these scores over time and extracting dynamic indicators we characterize group development and compare model outputs to expert human assessments. Results show that while GPT-4o and Gemini Flash 2.0 demonstrate reasonable agreement with human ratings, they differ in temporal sensitivity and responsiveness. The study underscores the potential of LLMs in supporting group dynamics analysis.