Aug 2026· Journal of Data and Information Science· 0 citations· 47 references
TL;DR
A theory-oriented framework that integrates KDD, knowledge-creation theory, human-AI collaboration, responsible generative AI, and epistemological reflection into a single-layered ecology is proposed.
Abstract
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.
It is argued that this understanding of human-GAI engagement, as explained through epistemological beliefs, lays the foundation for alternative approaches to teaching and assessment, student interactions, professional development, and AI governance and policy, while noting that the framework remains an exploratory heuristic requiring empirical validation.
S. Strydom· Journal of Applied Learning...· 0 citations
Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence, makes discovery more traceable, reproducible, and cumulative while preserving human creativity, judgment, and responsibility.
Xinjie Yao, Xingxin Xu, Xiyuan Gao et al.· 0 citations
It is argued that understanding AI knowledge creation is essential for bridging traditional human KM with the emerging discipline of AI Knowledge Management, and for designing governance structures that account for the inherent incompleteness and inconsistency of LLM knowledge.
T. Nguyen· European Conference on Knowl...· 0 citations
This article develops a sociology of generative knowledge for the age of AI. It treats contemporary systems as hybrid actors within sociotechnical networks and reframes classical anchors – Mannheim’s situatedness, Merton’s norms, and Latour’s distributed agency – for model-mediated inquiry. Generative knowledge is defined as knowledge organized to produce further knowledge through iterative, tool-mediated, and socially embedded processes. On this basis, the paper advances epistemic stewardship as a practical orientation that sustains human agency while harnessing computational acceleration. Stewardship is operationalized through transparency-by-design, provenance and traceability, calibrated trust, independent verification and red teaming, structured challenge routines, inclusivity in data and participation, and proportional delegation to machines. The account clarifies gains in discovery and education alongside risks from opacity, automation bias, feedback loops, and cognitive drift, and it specifies institutional reforms: standardized disclosure artifacts, replication triggers for AI-assisted claims, revised authorship taxonomies, and equitable access to compute, benchmarks, and community-governed datasets. A research agenda follows, calling for comparative evaluations of stewardship designs, longitudinal studies of hybrid practice, and field-specific protocols that link evaluation to adoption thresholds. The result is a framework that integrates social theory with design and governance guidance, aiming to convert acceleration into certified advance while keeping responsibility legible and contestable.
Paolo Granata· Journal of Emerging Perspect...· 0 citations
Recent advances in generative AI, particularly large language models and multimodal foundation models, have renewed interest in whether tacit knowledge can be learned or reproduced by machines. While prior studies emphasize the growing ability of generative AI to approximate patterns of human reasoning, judgment, and action, comparatively little attention has been paid to what aspects of tacit knowledge are excluded by design. This paper addresses this gap by asking a conceptual question: which aspects of tacit knowledge are structurally excluded from contemporary generative AI research? Rather than treating tacit knowledge as a single implicit capability, this study reorganizes prior research into three analytical perspectives: mediation, structure, and representation. From this viewpoint, tacit knowledge is sustained by processes that translate practice into communicable forms, by social and cultural structures that stabilize judgment and action, and by representational practices that constitute tacit knowledge as an object of analysis. These perspectives are then used to examine recent developments in generative AI. The analysis shows that current generative AI systems primarily engage with the externalized outcomes of tacit knowledge, such as observable reasoning patterns or action trajectories, while leaving its formative conditions unaddressed. Processes of mediation, social and institutional structures, and reflexive representational practices remain outside model design. These limitations are not merely technical but reflect structural design choices embedded in contemporary AI research. By clarifying these boundaries, this paper provides a conceptual framework for reconsidering the division of roles between human practice and generative AI in future socio-technical systems.
The rapid integration of Generative Artificial Intelligence (Gen-AI) into the built environment is transforming how knowledge is produced, interpreted, and applied in pursuit of sustainability. This study examines the emerging epistemological shifts associated with AI-mediated design and planning processes, with particular attention to how diverse knowledge systems interact in shaping sustainable built environment outcomes. This study aims to examine how Gen-AI mediates between human expertise, local and indigenous knowledge, and environmental data; identify epistemic opportunities and risks associated with AI-driven knowledge production; and propose a conceptual framework for sustainable knowledge creation in the Gen-AI era. A qualitative conceptual methodology was adopted, and a structured literature synthesis was conducted. The analysis focused on identifying patterns in knowledge inputs, AI mediation processes, and sustainability outcomes. The analysis identifies three key epistemic dynamics: epistemic augmentation, where AI enhances human analytical and design capacity; epistemic displacement, where algorithmic outputs risk overshadowing tacit or contextual knowledge; and epistemic justice, emphasising the need for inclusive integration of local and indigenous knowledge in AI-mediated systems. This study concludes that sustainable built environment practice in the Gen-AI era requires hybrid knowledge systems in which AI complements rather than replaces human and community-based expertise. The proposed conceptual framework highlights the interaction between diverse knowledge inputs, AI-mediated transformation, and sustainability-oriented design outcomes. It is recommended that future AI applications in the built environment prioritise transparent algorithms, participatory knowledge integration, and ethical governance to ensure context-sensitive and socially inclusive sustainability solutions.
MUSA MUSTAPHA DANRAKA, ZAKI BLESSED MAZADU, AMINA ADAMU et al.· Journal of Biodiversity and...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.