Epistemic Negotiations in AI Integration: A Typological Analysis of Educators’ AI-Enhanced Knowledge Management Practices
Sashi Ranjan, V. P. JoshithAs artificial intelligence reshapes the epistemic foundations of teaching, educators face unprecedented decisions about which knowledge practices to preserve, delegate or transform. This study introduces ‘epistemic negotiation’ as a conceptual lens to examine how educators actively deliberate about AI’s boundaries in knowledge work rather than passively accepting or rejecting technology. Moving beyond adoption-focused research, the study presents a typological analysis of AI-mediated Knowledge Management (KM) amongst 454 Indian educators. Using exploratory factor analysis and k-means clustering, four distinct profiles were identified: Disengaged, Instrumental Users, Process-Focused and High-fidelity Users, differentiated by epistemic orientations rather than technical competence. The findings reposition AI integration as primarily a knowledge management challenge. Theoretically, the study advances KM frameworks for analysing educational technology. Practically, the findings demonstrate that professional development must cultivate epistemic reflexivity and KM competencies over technical skills. For policy, the typology challenges uniform mandates, advocating differentiated support structures that honour diverse knowledge practices whilst establishing ethical parameters.