A4 Refereed article in a conference publication

Generative AI as a Socio-Technical Facilitator: A Case Study of AI-Supported Group Formation and Co-Creation




AuthorsPohjola, T.; Maijanen, E.; Sjöberg, M.

EditorsBabic, Snjezana; Car, Zeljka; Cicin-Sain, Marina; Ergovic, Pavle; Galinac Grbac, Tihana; Gros, Stjepan; Jovic, Alan; Jurekovic, Darko; Katulic, Tihomir; Koricic, Marko; Kralj, Nenad; Mornar, Vedran; Petrovic, Juraj; Skala, Karolj; Skvorc, Dejan; Sruk, Vlado; Tijan, Edvard; Valacich, Joe; Vrcek, Neven; Vrdoljak, Boris

Conference nameMIPRO ICT and Electronics Convention

Publication year2026

Journal: International Convention on Information and Communication Technology, Electronics and Microelectronics

Book title 2026 49th MIPRO ICT and Electronics Convention (MIPRO)

Volume49

First page 1178

Last page1183

ISBN979-8-3315-6310-3

eISBN979-8-3315-6309-7

ISSN1847-3938

eISSN1847-3946

DOIhttps://doi.org/10.1109/MIPRO70003.2026.11591640

Publication's open availability at the time of reportingNo Open Access

Publication channel's open availability No Open Access publication channel

Web address https://ieeexplore.ieee.org/document/11591640


Abstract

While generative AI is widely adopted as an individual productivity tool, its potential to recognize competences and orchestrate collective processes (e.g., group formation and collaborative innovation) remains underexplored. This paper presents a case study examining how a custom AI facilitation tool supported co-creation among diverse creative professionals at a European workshop event. Research connects to Service-Dominant logic and the concept of operant resource integration. First, the AI tool structured conversational assessments to map participants' competencies, goals, and constraints-generating individualized "upskilling profiles" that captured both explicit skills and tacit professional orientations. Second, it used these profiles to form heterogeneous working groups with complementary expertise and shared intentions, then facilitated iterative co-creation through reflective prompts, structural suggestions, and real-time synthesis. Our empirical data comprises 34 AI-participant interaction logs and 8 group facilitation transcripts, analyzed using thematic analysis. Findings suggest that AI-supported profiling accelerated resource mapping and reduced barriers to articulating tacit knowledge. Secondly, algorithmically formed groups demonstrated productive complementarity, though success depended on participants' engagement with the AI interface. The paper contributes a concrete example for AI-enhanced collaboration that preserves participant agency while enabling collective sense-making in a digital economy where dynamic team formation and resource integration are critical.



Last updated on 03/08/2026 09:24:51 AM