4-Slide Mini Deck · Emergent Novelty
Emergent Novelty, in four slides
Preprint, not peer reviewed · SSRN 7234219
Slide 1 of 4
The Problem
Studies of human-AI creative collaboration almost always use naive participants in a single short session, which cannot detect what happens after an operator develops sustained fluency with AI collaboration.
Slide 2 of 4
The Emergent Novelty Contribution
Emergent Novelty (EN): output attributable to neither the human nor the AI alone. A three-question Novelty Attribution Test, the ENR metric, and a five-phase developmental model (Discovery, Emergence, the Hinge, Formalization, Centaur).
Slide 3 of 4
How It Works · Evidence Status
Anchored to a real external study (Shin et al. 2023, PNAS, Go players). The paper's own evidence: one operator, 18 deliverables, retroactively self-classified — strict ENR 83.3%.
Evidence status: Preliminary single-case evidence, evaluated with a disclosed conflict of interest.
Slide 4 of 4
Why It Matters · Limits · Open Questions
If validated, suggests a human-AI dyad can function as a distinct generative system. The author explicitly invites replication and critique.
Limits: single case, self-evaluated, not independently validated. Open: generalizability; the untested neurodivergent-advantage hypothesis.
Preble, Micheal Charles. “Emergent Novelty in Human-AI Dyadic Systems: A Theoretical Framework and Preliminary Evidence” Available at SSRN 7234219, 2026.