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Research in Neuro-Information Systems (NeuroIS) integrates neuroscience methods with Information Systems (IS) research to advance our understanding of human-technology interactions. As neurophysiological data collection methods evolve, the analysis of large and complex data sets remains a significant challenge. Generative Artificial Intelligence (GenAI) offers new opportunities for NeuroIS by improving data analysis, experimental design, and interpretation of neural patterns. This paper systematically reviews 56 studies applying GenAI in NeuroIS and identifies five main research themes: (1) GenAI for Autonomic Nervous System Measurements, (2) GenAI for Brain Research, (3) GenAI for General Applications, (4) GenAI for Genetics, and (5) GenAI for Multimodal Approaches. Our findings highlight how GenAI improves data interpretation, integration, and processing while streamlining the use of research methods. Overall, this review underscores the transformative potential of GenAI in NeuroIS, paving the way for scalable, precise, and dynamic research methodologies in the field.

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The Role of Generative Artificial Intelligence in the NeuroIS Research Process: Applications and Opportunities

  • 会议论文

  • 发表日期：

    2025年

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  • Leonardo Banh,
  • Fabian J. Stangl,
  • Gero Strobel,
  • René Riedl

摘要

Research in Neuro-Information Systems (NeuroIS) integrates neuroscience methods with Information Systems (IS) research to advance our understanding of human-technology interactions. As neurophysiological data collection methods evolve, the analysis of large and complex data sets remains a significant challenge. Generative Artificial Intelligence (GenAI) offers new opportunities for NeuroIS by improving data analysis, experimental design, and interpretation of neural patterns. This paper systematically reviews 56 studies applying GenAI in NeuroIS and identifies five main research themes: (1) GenAI for Autonomic Nervous System Measurements, (2) GenAI for Brain Research, (3) GenAI for General Applications, (4) GenAI for Genetics, and (5) GenAI for Multimodal Approaches. Our findings highlight how GenAI improves data interpretation, integration, and processing while streamlining the use of research methods. Overall, this review underscores the transformative potential of GenAI in NeuroIS, paving the way for scalable, precise, and dynamic research methodologies in the field.