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

Leonardo Banh (), Fabian J. Stangl (), Gero Strobel () and René Riedl ()
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Leonardo Banh: University of Duisburg-Essen, Rhine-Ruhr Institute of Information Systems
Fabian J. Stangl: University of Applied Sciences Upper Austria, Digital Business Institute, School of Business and Management
Gero Strobel: University of Duisburg-Essen, Rhine-Ruhr Institute of Information Systems
René Riedl: University of Applied Sciences Upper Austria, Digital Business Institute, School of Business and Management

A chapter in Information Systems and Neuroscience, 2025, pp 27-43 from Springer

Abstract: Abstract 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.

Keywords: Generative AI (GenAI); Human-AI interaction; Neuro-Information systems (NeuroIS); Neurophysiological measurements; Literature review (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-00815-2_3

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