KSC: Knowledge, Structure, and Constraint — A Functional Framework for Human–AI Differences and Collaboration

Current AI systems can draw on broad knowledge and rapidly generate complex structures, yet they may still lose track of goals, premises, boundaries, or acceptance criteria in long, multi-step tasks involving multiple conditions and strong state dependence. Why the expansion of knowledge and generative capability has not translated proportionally into task reliability is a key question for understanding human–AI differences and the mechanisms of collaboration. This paper proposes the Knowledge–Structure–Constraint Framework (KSC), which interprets intelligent activity in complex goal-directed tasks as a three-tier functional relationship of information. K denotes the information and state available to the system during a task; S denotes structure generation based on K, including the formation of problem representations, relationships, solutions, and action paths; and C denotes the governance of S and the state transitions it induces by means of goals, premises, boundaries, and acceptance criteria. K provides the material for S, S produces candidate structures, and C determines whether those structures can be accepted, carried forward, or revised. The three are functionally distinguishable but may be jointly implemented by the same agent or component. KSC distinguishes the functional tier that information occupies in the current task rather than dividing information content into three mutually exclusive categories. Both S and C are grounded in K and constitute higher-order relations of information generation and control over generation, respectively. On this basis, the paper advances a provisional comparative claim: current AI has comparative advantages in the breadth of knowledge access and in the speed and scale of structure generation; humans, because they remain continuously embedded in real-world environments and face responsibility, feedback, and the consequences of action, are generally better able to keep goals and boundaries effective over time. AI difficulties with C can arise both within a single reasoning process and in the way constraints are stored, retrieved, and updated by external systems. Even when constraints re-enter the context, a model may still fail to continue following them in later stages. KSC does not require each failure to be assigned uniquely to a single tier; the same problem may propagate continuously across K, S, and C. The key to human–AI collaboration is for humans, AI, and external systems to share responsibility for K, S, and C rather than presupposing a fixed division of labor. In the engineering tasks emphasized in this paper, current AI can often take on substantial portions of K and S, so the key incremental human contribution appears more often in C. When the problem representation or the direction of structure generation is itself unresolved, humans may also participate directly in S; that case is not developed here. Provided that K and S already meet task requirements, further expansion of AI autonomy depends on the range of tasks over which the system as a whole can acquire and preserve the necessary constraint information and keep those constraints continuously enforced, updated, and verified. This paper analyzes complex tasks whose goals and evaluation criteria have already been established or confirmed through collaboration. KSC is a functional coordinate system for comparing humans, AI, and hybrid systems; it is not a theory of consciousness, agency, or the origin of values.