preprint OISI26 t-14 inst_AI5 - We Converse, Therefore We Become: A Constructivist Theory of Human–AI Dialogical Knowledge Formation

Abstract

@font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}@font-face {font-family:Aptos; panose-1:2 11 0 4 2 2 2 2 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:536871559 3 0 0 415 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin-top:0cm; margin-right:0cm; margin-bottom:8.0pt; margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Aptos",sans-serif; mso-ascii-font-family:Aptos; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Aptos; mso-fareast-theme-font:minor-latin; mso-hansi-font-family:Aptos; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-font-kerning:1.0pt; mso-ligatures:standardcontextual; mso-fareast-language:EN-US;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-family:"Aptos",sans-serif; mso-ascii-font-family:Aptos; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Aptos; mso-fareast-theme-font:minor-latin; mso-hansi-font-family:Aptos; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;}.MsoPapDefault {mso-style-type:export-only; margin-bottom:8.0pt; line-height:115%;}div.WordSection1 {page:WordSection1;} Generative artificial intelligence systems are increasingly embedded in the conversational environments through which researchers and organisations construct knowledge. While large language models (LLMs) are typically conceptualised as tools for information retrieval or text generation, this paper argues that their epistemic role cannot be adequately understood within a positivist framework. Drawing on phenomenology, constructivism, and socio-technical research, the paper develops a theory of dialogical human–AI collaboration in which knowledge emerges through recursive interaction rather than being retrieved from data. Building on Heidegger’s insight that understanding arises through participation in discourse, we propose that human–AI interaction constitutes a form of epistemic co-construction grounded in structural coupling and consensus domains. In this view, AI systems do not “understand” in the human sense, but can participate in the formation of meaning when embedded within dialogical processes involving human agents situated in communities of practice. The paper introduces the concept of constructivist AI collaboration, in which generative systems function as epistemic amplifiers within socio-technical knowledge processes. It then develops a set of design and research implications, arguing that the effective use of AI in human and social sciences requires infrastructures that support dialogical interaction, contextual grounding, and accountable communication. By reframing AI from a tool for knowledge retrieval to a participant in epistemic evolution, the paper contributes to Information Systems research by linking AI, socio-technical theory, and constructivist epistemology into a unified framework for understanding knowledge formation in the age of generative systems.