Paper Type
ERF
Abstract
We develop a pragmatist lens to theorize how human-AI collaboration fosters organizational knowledge creation. Building on Charles S. Peirce’s concept of inquiry, we introduce Epistemic Human-AI Collaboration (EHAIC), a dynamic process where human reasoning and algorithmic operations co-evolve through cycles of doubt, hypothesis, and testing. While existing theories such as sociomateriality emphasize human-technology entanglement, they underexplore the epistemic dimension of knowledge production with adaptive, opaque AI systems. Through two healthcare case studies, we illustrate how AI provokes new inquiries, refines expertise, and transforms organizational practices. EHAIC emphasizes iterative knowledge construction, collective inquiry across stakeholders, and the productive role of opacity in fostering deeper learning. Our approach extends organizational theory by centering on how humans and AI systems co-construct knowledge over time, offering insights for managing emerging forms of expertise.
Paper Number
2010
Recommended Citation
perez torrents, joel and Altmejd, Simon, "Human-AI Knowledge Creation Through a Peircean Lens" (2025). AMCIS 2025 Proceedings. 28.
https://aisel.aisnet.org/amcis2025/sig_osra/sig_osra/28
Human-AI Knowledge Creation Through a Peircean Lens
We develop a pragmatist lens to theorize how human-AI collaboration fosters organizational knowledge creation. Building on Charles S. Peirce’s concept of inquiry, we introduce Epistemic Human-AI Collaboration (EHAIC), a dynamic process where human reasoning and algorithmic operations co-evolve through cycles of doubt, hypothesis, and testing. While existing theories such as sociomateriality emphasize human-technology entanglement, they underexplore the epistemic dimension of knowledge production with adaptive, opaque AI systems. Through two healthcare case studies, we illustrate how AI provokes new inquiries, refines expertise, and transforms organizational practices. EHAIC emphasizes iterative knowledge construction, collective inquiry across stakeholders, and the productive role of opacity in fostering deeper learning. Our approach extends organizational theory by centering on how humans and AI systems co-construct knowledge over time, offering insights for managing emerging forms of expertise.
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