TriKoNet: The Trivalence Model of Potential Co-Creativity in Socio-Technical Networks

Abstract

This article introduces the TriKoNet , a model based on Actor-Network Theory that conceptualises creativity not as an individual attribute but as an emergent effect of sociotechnical networks. The study is grounded in an exploratory, two-phase investigation in which pedagogical avatars were designed within a creative network; TriKoNet models creativity as a processual triadic interplay of stabilisation, destabilisation, and re-stabilisation within a network of human and non-human actors. Following Actor-Network Theory, the study reconstructs how the avatar designs emerged through problematisation, interessement, enrolment, and mobilisation. The findings show that the avatar triad concept was not the result of individual creative acts but was constituted through iterative translation processes among students, researchers, and technical artefacts such as collaborative design tools and generative AI systems.TriKoNet allows for diagnosing the extent to which technical artefacts stabilise, destabilise, or re-stabilise creative learning processes under three conditions: shared cultural reference resources, opportunities for meta-communication, and structurally open scripts. From this, the article derives four preliminary design principles and a modular design framework that connects stable structural principles (role logic, technical presence, claim to inclusion) with variable appropriation space – serving as a starting point for translating TriKoNet into a design instrument to be tested later, rather than its endpoint. AI agents are conceptualised here as equal, constitutive network participants.This article introduces the TriKoNet, a model based on Actor-Network Theory that conceptualises creativity not as an individual attribute but as an emergent effect of sociotechnical networks. (A more extensive theoretical grounding and empirical application of TriKoNet is developed in the author's forthcoming dissertation, submitted September 2026 at Humboldt-Universität zu Berlin; this article presents a condensed account of its core ideas and findings.)