Exploring the NeuroIS Potential for Generative Artificial Intelligence: Findings from a Literature Review

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 11-25 from Springer

Abstract: Abstract Generative Artificial Intelligence (GenAI) is transforming human–computer interaction, shaping behavior as well as cognitive and emotional processes. This paper explores how Neuro-Information Systems (NeuroIS) measurements can be applied to the study of GenAI, addressing their role in human-AI interaction. Through a literature review, we identify 21 papers using neurophysiological measurements, including autonomic nervous system (ANS) markers (e.g., eye-tracking), brain imaging (e.g., EEG), and multimodal approaches such as combining eye tracking and EEG. Our findings highlight main research themes, including cognitive offloading, trust, and decision-making biases in human interaction with GenAI. While research on this topic is becoming more prominent, neurophysiological investigations remain limited. We anticipate that measures of brain and ANS system activity, as well as hormone measures, will play an increasing role in future empirical research on GenAI. This study contributes to the advancement of NeuroIS by providing a structured foundation for better understanding the role of GenAI in this research 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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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-00815-2_2

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DOI: 10.1007/978-3-032-00815-2_2

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