AI-Human Duoethnography for Educational Leadership: Reflexivity Beyond the Human in Postdigital Contexts

This paper introduces AI-Human Duoethnography as a methodological response to the entangled conditions of postdigital educational leadership. While duoethnography traditionally relies on the juxtaposition of two human lives, this study extends the method by engaging an artificial intelligence system as a structured interlocutor within the lived experience of a middle leader. Through a reflexive cycle involving dialogic exchanges, identity mapping, and entanglement analysis, the method surfaces tensions, identity movements, and sociomaterial paradoxes that shape contemporary leadership practice. The findings demonstrate how AI’s provocations, reframings, and misfires function as productive disruptions that deepen reflexive inquiry, particularly in relation to emotional labour, role negotiation, and institutional contradiction. AI-Human Duoethnography reframes leadership reflexivity as sociomaterial labour distributed across human experience, organisational context, and algorithmic patterning. The paper discusses the methodological, ethical, and practical implications of this approach for leadership development and professional learning, and identifies limitations and future directions. AI-Human Duoethnography contributes a methodological innovation and conceptual reorientation, offering a way to study leadership reflexivity beyond the human and to critically engage with the hybrid realities of postdigital education.