Knowledge practices and generative artificial intelligence in management education: adopters, facilitators, and inhibitors

Purpose This study aims to advance knowledge management (KM) scholarship by theorizing how generative artificial intelligence (GenAI) reshapes knowledge practices in management education. Prior work on GenAI in higher education has been largely descriptive, mapping faculty experiences onto generic technology-adoption constructs while leaving its KM consequences under-theorized. Design/methodology/approach The authors address this gap by developing a process model of GenAI-mediated knowledge practices organized around three interrelated dynamics, namely knowledge integration, knowledge orchestration and epistemic risk. This study uses Dual Factor Theory and the Unified Theory of Acceptance and Use of Technology 2 as sensitizing frameworks rather than as the contribution itself. Findings The findings show that GenAI catalyzes knowledge integration by fusing artificial intelligence (AI)-generated content with disciplinary expertise, exposes weaknesses in knowledge orchestration where policy voids and uneven digital literacy fragment institutional coordination and generates qualitatively new epistemic risks such as hallucinated citations, opaque authorship and selective disclosure of AI use. Originality/value The research re-specifies dual-factor dynamics for GenAI-mediated knowledge work, demonstrating that enablers and inhibitors operate as independent epistemic forces rather than as opposing poles. Also, they extend KM scholarship on knowledge risk by identifying a class of AI-specific risks that conventional governance instruments are not designed to absorb. Finally, they offer practitioner-relevant guidance for academic leaders seeking to orchestrate responsible GenAI integration in knowledge-intensive institutions.