On an Alternative Class of Human–AI Interaction

Artificial intelligence systems are rapidly becoming integral to human decision-making. The dominant research focus has been placed on accuracy, performance, and scalability. Far less attention has been given to a more fundamental question: what happens to human thinking, responsibility, and agency when intelligent systems become cognitive reference points rather than tools. This work does not argue that existing AI systems are wrong, dangerous, or misguided. It argues that the architecture of interaction itself has been insufficiently examined. The paper introduces and formally fixes an alternative research direction: a class of human–AI interaction systems designed not to provide answers, but to preserve human will through probabilistic navigation and explicit uncertainty. The proposed system does not decide, does not assert truth, and does not optimize outcomes. It structures thinking around hypotheses, likelihoods, and verifiable actions in reality, leaving responsibility entirely with the human. A minimal experimental instrument—a decision-navigation planner—is presented as a practical means to test this interaction vector outside of theory. This publication is intentionally positioned as a working paper. Its purpose is not to prove correctness, but to open and fix a research trajectory whose implications unfold over years and decades. If the problem described here exists, ignoring it will not eliminate its consequences. If it does not, reality will make that equally clear.