KNIT: Computational Boundary Objects for Real-Time Convergence in Interdisciplinary Teams

Interdisciplinary teams developing complex technologies such as healthtech struggle to align disciplinary perspectives, stakeholder priorities, and evolving problem framings, particularly during rapid iteration, when existing collaboration tools offer limited support for in-session negotiation. We present KNIT, an AI-mediated framework that conceptualises AI-generated artefacts as computational boundary objects. KNIT supports convergence by externalising anonymised individual inputs into shared artefacts, including semantic clusters and stakeholder-centred problem reframings, that surface differences in interpretation and make them available for negotiation. We evaluated KNIT in workshops with seven early-stage healthtech teams (28 participants), analysing 190 interaction episodes using Carlile’s 3T framework. KNIT supported knowledge boundary crossing across syntactic (95.0%), semantic (86.3%), and pragmatic (84.8%) levels. We contribute empirical evidence and design principles showing how computational boundary objects mediate distinct boundary-crossing mechanisms, demonstrating that representational transformation rather than automation is the primary mechanism through which AI enables convergence across disciplinary boundaries.