Why Is Creative Collaboration With Generative AI Actually Possible? A Theoretical Analysis With Social System Theory and Creative System Theory

Takashi Iba ()
Additional contact information
Takashi Iba: Keio University, Faculty of Policy Management

A chapter in Artificial Intelligence and Networks for a Sustainable Future, 2026, pp 173-194 from Springer

Abstract: Abstract This paper presents a systems-theoretical analysis of creative collaboration between humans and generative AI. Interactive generative AI provides meaningful responses to human inputs and generates ideas, leading many users to perceive it as a “partner” in creative collaboration. How, then, is creative collaboration possible with generative AI despite the absence of consciousness resembling that of humans? To answer this question, this paper examines what is actually occurring from the perspectives of sociologist Niklas Luhmann’s Social Systems Theory and the Creative Systems Theory that I previously proposed. These systems theories conceptualize social communication and creative discovery as autopoietic systems with semantic contexts that operate independently of the human mind. Through these theoretical lenses, it becomes clear that while generative AI systems differ from human psychic systems, they can—through Large Language Models (LLMs) enabling sequential linguistic analysis and generation—serve as the functional equivalent of psychic systems by facilitating the synchronization of meaning within the sequential development of social communication and creative discovery. The paper concludes that the sense that interactions and co-creation with generative AI feel human-like should not be regarded as a mere illusion; rather, generative AI actually functions as a legitimate counterpart with whom one can jointly advance communication and discovery.

Keywords: Generative AI; Collaboration; Systems theory; Autopoiesis (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-032-13458-5_11

Ordering information: This item can be ordered from
http://www.springer.com/9783032134585

DOI: 10.1007/978-3-032-13458-5_11

Access Statistics for this chapter

More chapters in Contributions to Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().