Submitted:
12 September 2026
Posted:
14 September 2026
You are already at the latest version
Abstract
As humans and AI agents work together across long-horizon tasks and accumulate interaction history, how cognitive work is organized on one task can shape both their later performance and later capability. Within a Human–AI system, the next step may involve further reasoning, retrieval from prior experience, verification, a tool call, another model, human judgment, or stopping. The cognitive operation used also shapes who gets what experience: whether the human continues to practice a skill, whether the AI receives demonstrations or corrections, which failures are exposed for supervision, and which trajectories become reusable. The way cognitive work is organized today therefore helps construct the Human–AI system that will act tomorrow. This survey connects human cognition, Human–Automation and HCI, and modern AI agents to explain how present cognitive organization shapes future Human–AI capability. Human cognition research explains how limited attention, computation, and control are organized, and how experience can restructure later cognition. HumanAutomation and HCI research shows how functions, authority, and initiative are divided across people and machines, and how that division changes human readiness and skill. In modern AI agents, many of these cognitive operations are explicit and programmable, while interaction trajectories can be retained and transformed into memories, workflows, skills, and policies that shape later behavior. Read together, these literatures expose a causal sequence: organizing cognitive work determines the distribution of practice, supervision, feedback, and trajectories; that experience changes human skill and beliefs, machine memory and policy, and the coordination between them; and the updated joint system then organizes later work differently. We argue that Human–AI collaboration should therefore be studied as a process of joint cognitive development. The design problem is to organize current cognition while accounting for the Human–AI system that the current choices are shaping. The way humans and AI work together today changes what each can do tomorrow.
Keywords:
Human--AI collaboration
; AI agents
; longitudinal learning
; continual learning
; metareasoning
; agent memory
; self-evolving agents
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.