Collaborative Conjecture
Collaborative Conjecture describes a reasoning practice in which humans and AI systems jointly explore, challenge, and refine hypotheses. The goal is not immediate answers, but the progressive clarification of ideas.
This phase corresponds closely to what is often described in research as hybrid intelligence — the collaboration between humans and AI systems.
However, while hybrid intelligence typically focuses on interaction, collaborative conjecture emphasizes the formation of reasoning artifacts that can be preserved, traced, and carried forward within a system.
Reasoning as Exploration
In many domains, useful ideas emerge through tentative proposals rather than final conclusions. A conjecture is a working hypothesis that can be tested, refined, or rejected.
AI systems can accelerate this exploratory process by generating perspectives, counterexamples, and alternative interpretations.
Dialogue Rather Than Delegation
Collaborative conjecture differs from simple AI delegation. Instead of asking systems to produce finished outputs, humans engage in an iterative dialogue that develops the reasoning itself.
Each exchange clarifies assumptions, surfaces implications, and improves the structure of the argument.
From Exploration to Record
Exploratory dialogue alone does not preserve reasoning over time. Without structured records, insights remain ephemeral.
Within the PKOS framework, collaborative conjecture often produces Pay-It-Forward Records (PIFRs), which capture the reasoning path behind emerging conclusions.
Relationship to Shadow AI
Where Shadow AI describes opaque AI participation in reasoning, collaborative conjecture represents its constructive counterpart.
Instead of invisible influence, the reasoning process becomes explicit and inspectable.
Part of the PKOS Lexicon.