Catalyst for co-construction: the role of AI-directed speech recognition technology in the self-organization of knowledge

Abstract

The purpose of this study was to analyze knowledge co-construction as a self-organization process and the role of technology as its catalyst. Novel AI-directed speech recognition technology and the artifacts it generates were deployed to scaffold the knowledge co-construction process in two groups of pre-service teachers in a science education context. Throughout the lesson, the focus of the learning tasks was on pedagogical content knowledge and students' preconceptions. Analysis was conducted through the key characteristics of the social system's self-organization theory. The process of self-organization refers to the system's capacity to diverge from familiar structures, perspectives, and operations. Through the lenses of system theories, the active role of artifacts in co-construction was grasped and the role of technology in the self-organization of knowledge was analyzed. The pedagogical design of knowledge co-construction followed the principles of student-engaging learning. The technology used in co-construction was novel speech recognition AI software, which produced visual and editable word cloud artifacts from oral discussions on the large-format screen to edit. The data included videos and audio recordings. In this qualitative study, a content analysis and interaction analysis were used with descriptive analysis. The results showed that when technology became visible, as an active component of the system, artifacts triggered key signs of the social system's self-organization in co-construction. Exchange of information, “entropy levels,” were rapidly increased, and different viewpoints were expressed. Also, “chaos zones,” far-from-equilibrium states, were reached in both groups. Editable artifacts on the screen represented bifurcation spaces where groups' discussions were crystallized for the first time. Information was further categorized and evaluated through artifacts and this demonstrated how the groups processed communication into learning insights. Based on the results, the role played by this kind of technology was significant in the self-organization of knowledge. Materialized artifacts pushed the groups from small group conversation phases, comfort zones, toward uncertainty and confusion, which are central in self-organization. Technology in the system is seen not only as an interactor but also as an active agent that can facilitate epistemic emotions and support the group in the self-organization of knowledge.