Hybrid Reasoning Systems That Prioritize and Enhance Human Intelligence
Abstract
In a world of accelerating change, there is a need for wise and adaptive human reasoning. Integrating AI capabilities with human guidance offers promise, though human reasoning itself is often hasty, shortsighted, and error-prone, and no clear framework exists for combining human reasoning strategies with AI across diverse tasks. This article proposes a framework for human-centered hybrid reasoning systems that engage and enhance human reasoning abilities ranging from granular data analysis to high-level reflection and wisdom. The framework was developed through a conceptual synthesis combining: (1) established strategies for enhancing human reasoning, (2) AI design approaches that favor pre-conclusive engagement over the generation of conclusions, and (3) the treatment of reasoning as a collection of distinct, individually supportable modes. This synthesis produced a distinctive framework for broad-spectrum reasoning enhancement from which a typology of reasoning modes, a system architecture, and a high-level research agenda were derived.
