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All Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Meyti Eka Apriyani
Department of Electrical Engineering and Informatics, State University of Malang, 65145, Indonesia
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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A Conceptual Framework for Human AI Collaboration: Ontological and Epistemological Perspectives Meyti Eka Apriyani; Syaad Patmanthara
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.251

Abstract

Collaboration between humans and artificial intelligence (AI) has become a pivotal phenomenon in the evolution of information systems, yet its philosophical foundations remain underexplored. This study develops an integrative conceptual framework that combines ontological and epistemological perspectives to examine how human–AI collaboration shapes knowledge creation and decision-making within sociotechnical contexts. The proposed framework identifies five ontological levels of AI agency and four epistemological processes underlying hybrid knowledge formation. It further integrates six interrelated dimensions—ontological, epistemological, technical, ethical, social, and organizational—that collectively define the dynamics of human–AI collaboration. The findings contribute to the theoretical discourse by introducing the constructs of quasi-epistemic entities and hybrid epistemology, which reconceptualize AI not merely as a computational artifact but as a participant in epistemic processes, thereby extending existing theories of distributed cognition and epistemic accountability beyond instrumental human–machine models. Practically, the framework informs the design of transparent, adaptive, and ethically aligned human–AI systems within information-intensive environments.
Co-Authors Syaad Patmanthara