From Human-Guided to Generative Knowledge Discovery: A Reflexive Human-AI Ecology Framework for the Age of Generative AI

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Abstract Purpose The discovery, interpretation, and transformation of knowledge into practical insight are being revolutionized by generative artificial intelligence. In light of these developments, this conceptual paper critically reexamines human-guided KDD and knowledge discovery in databases. As a revised framework for understanding knowledge discovery in human AI environments, it suggests Generative Knowledge Discovery in Databases, or Gen-KDD. Design/methodology/approach Using a conceptual approach based on envisioning, the paper seeks to advance theory. In order to create a layered framework that incorporates process, agency, governance, and epistemological reflection, it synthesizes literature on KDD, knowledge creation, human AI collaboration, responsible AI, and generative AI. Findings Knowledge discovery is rethought as a generative and reflexive human AI ecology by the suggested Gen-KDD framework. Generative contextualization, autonomous data curation, generative feature engineering, generative discovery, and co-evolutionary sensemaking are its five stages. These stages are arranged according to human-dominant, AI-dominant, and hybrid layers, making it clear where AI can take the lead, where human judgment is still crucial, and where shared sensemaking is necessary. Research limitations Rather than providing actual evidence, the paper provides a conceptual framework. Future studies should operationalize Gen-KDD across domains and investigate how it affects organizational learning, accountability, transparency, fairness, and decision quality. Practical implications Gen-KDD’s design guidelines can be used by organizations that want to ethically incorporate generative AI into analytics and decision-making. It demonstrates how human oversight, ethics, and governance can be integrated into discovery processes rather than being added as external controls. Originality/value This paper advances a theory-oriented framework that integrates KDD, knowledge-creation theory, human-AI collaboration, responsible generative AI, and epistemological reflection into a single-layered ecology. Gen-KDD offers a richer foundation for knowledge discovery than linear process models by explicitly addressing the changing roles and limits of both human and machine cognition.

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Related topics: Ethics and Social Impacts of AI · Big Data and Business Intelligence · Innovation, Sustainability, Human-Machine Systems