An approach based on hybrid-augmented intelligence for the combination and optimization of human-machine teams

Recent advances in Large Language Models (LLMs) have demonstrated their unparalleled capability in collaborative design requirements mining, offering significant potential for the integration of Human–Machine Teams (HMTs) and improved efficiency in design mining processes. However, existing approaches often lack integrated frameworks capable of simultaneously addressing both the compositional and operational challenges of LLM–human teams, which hinders their effective deployment in complex, real-world scenarios. Specifically, two critical challenges remain: first, how to effectively transform LLMs into reliable domain experts capable of understanding and elaborating design requirements; and second, how to optimize HMT configurations amid inherent ambiguities in human expert evaluations. To address these gaps, we propose a novel dual-paradigm Hybrid-Augmented Intelligence (HAI) framework that integrates Cognitive Computing (CC-HAI) with Human-in-the-Loop (HITL-HAI) mechanisms. Our key contributions include a CC-HAI–based cognitive teammate mechanism that uses structured prompt engineering to transform LLMs into domain-specialized roles, facilitating the formation of collaborative HMTs; and an HITL-HAI uncertainty mitigation method that employs a Z-number-enhanced cloud modeling approach to manage subjective uncertainties in expert assessments and support robust team configuration. The framework is validated through multi-domain case studies spanning smart home systems, smart cockpits, medical devices, and baby products. Extensive experiments demonstrate its effectiveness in terms of team performance, error reduction, cross-domain generalizability, and decision-making superiority. This research provides a replicable paradigm for deploying LLMs as cognitive collaborators in collaborative design ecosystems, contributing to both theory and methodology in human–machine team intelligence.