Paper Type
ERF
Abstract
Generative artificial intelligence (GenAI) can take on humans' responsibilities of information processing for expected outcome, which can enhance work performance. However, potential risks in GenAI-generated outcomes, such as hallucinations, can be amplified by cognitive offloading, a process in which humans shift their cognitive effort to GenAI. To examine the antecedents and outcomes of cognitive offloading in human-GenAI collaboration, we conducted interviews with 45 working professionals at a U.S. public university. We propose an inductive model derived from our data. We found that increased cognitive offloading can reduce human engagement through uncritical acceptance of GenAI outputs, thereby reducing human compensatory behaviors. Our study is expected to provide an initial theoretical understanding of cognitive offloading in human-GenAI collaboration, as well as practical insights to support more effective GenAI use in workplace settings.
Paper Number
1741
Recommended Citation
Xu, Haoyue; Xue, Yue; and Tarafdar, Monideepa, "Cognitive Offloading in Human-GenAI Collaboration" (2026). AMCIS 2026 Proceedings. 15.
https://aisel.aisnet.org/amcis2026/sig_osra/sig_osra/15
Cognitive Offloading in Human-GenAI Collaboration
Generative artificial intelligence (GenAI) can take on humans' responsibilities of information processing for expected outcome, which can enhance work performance. However, potential risks in GenAI-generated outcomes, such as hallucinations, can be amplified by cognitive offloading, a process in which humans shift their cognitive effort to GenAI. To examine the antecedents and outcomes of cognitive offloading in human-GenAI collaboration, we conducted interviews with 45 working professionals at a U.S. public university. We propose an inductive model derived from our data. We found that increased cognitive offloading can reduce human engagement through uncritical acceptance of GenAI outputs, thereby reducing human compensatory behaviors. Our study is expected to provide an initial theoretical understanding of cognitive offloading in human-GenAI collaboration, as well as practical insights to support more effective GenAI use in workplace settings.
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