Paper Type
ERF
Abstract
The rapid diffusion of generative AI is creating "forensic opacity" from synthetic images, driving an accelerating integrity crisis in academic publishing. The widespread adoption of AI as a cocreator has outpaced the editorial review process's verification capacity. This emergent study uses the theoretical perspectives of the AI Diffusion Triad and Service-Dominant Logic to explore the demarcation between human-AI augmentation and fraudulent substitution. We argue that the AI-driven productivity shock overwhelms the capacity of the traditional peer-review process. Undisclosed AI displacement of cognitive labor also shifts the ecosystem from value co-creation to value co-destruction, leading to a convergence toward template-like research generation. This ERF proposes an acceptance boundary framework and an empirical research agenda to identify the acceptable human/AI boundary and help re-establish trust and accountability in scholarly content generation.
Paper Number
1552
Recommended Citation
Ahrens, Fred and Hong, Paul C., "From Augmentation to Substitution: Governing Human–AI Boundaries in Generative Ecosystems" (2026). AMCIS 2026 Proceedings. 16.
https://aisel.aisnet.org/amcis2026/sig_dsa/sig_dsa/16
From Augmentation to Substitution: Governing Human–AI Boundaries in Generative Ecosystems
The rapid diffusion of generative AI is creating "forensic opacity" from synthetic images, driving an accelerating integrity crisis in academic publishing. The widespread adoption of AI as a cocreator has outpaced the editorial review process's verification capacity. This emergent study uses the theoretical perspectives of the AI Diffusion Triad and Service-Dominant Logic to explore the demarcation between human-AI augmentation and fraudulent substitution. We argue that the AI-driven productivity shock overwhelms the capacity of the traditional peer-review process. Undisclosed AI displacement of cognitive labor also shifts the ecosystem from value co-creation to value co-destruction, leading to a convergence toward template-like research generation. This ERF proposes an acceptance boundary framework and an empirical research agenda to identify the acceptable human/AI boundary and help re-establish trust and accountability in scholarly content generation.
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