Human-AI Collaborative Systems: Cognitive Computing Approaches for Enhancing User Interaction and Decision Support

Abstract

Human-AI Collaborative Systems (HACS) are an important paradigm shift in the field of computational intelligence in the sense that they go beyond entirely autonomous artificial intelligence in favor of synergistic human-computer cooperation. The article introduces a detailed theoretical basis of HACS based on the principles of cognitive computing, combining the use of Bayesian decision networks with transformer-based attention models, reinforcement learning using human feedback (RLHF) and adaptive interface models. A new Cognitive Collaboration Score (CCS) is proposed as a single measure of performance of collaborative systems through the combined consideration of the accuracy of the task, cognitive load, calibration of trust, and system responsiveness. There were three areas of decision-making in which the proposed framework was empirically tested: clinical diagnostics (2,840 cases), financial portfolio optimization (1,920 sessions), and autonomous vehicle path planning (3,120 scenarios). Experimental outcomes also indicate that the HACS framework proposed has a mean accuracy gain of 23.4 percentage points compared to unaided human judgement, and 18.7 percentage points compared to wholly autonomous AI systems, and significant performance gains are statistically significant. Moreover, the cognitive workload, determined with the help of NASA-TLX scale, was decreased by 31.2, and the trust calibration score was 0.847 with the standard deviation of 0.031. The results validate the claim that uncertainty-sensitive Human-AI interaction leads to a substantial performance in the quality of decisions, user trust, and cognitive efficiency in complex settings. The offered framework offers practical design concepts to next-generation intelligent decision-support solutions and adaptable human-centered AI interfaces.