Educational leadership has long been seen as an inherently lonely endeavour. For teacher-researchers, traditional reflective practices like autoethnography or journalling often devolves into an echo chamber, lacking the “critical friend” necessary to combat subjective biases. This paper introduces a novel methodological framework: AI-Human Duoethnography. By prompting a Large Language Model (LLM) to inhabit a “Researcher Persona”, this study demonstrates how an isolated educational leader can engage in a rigorous, dialogic interrogation of their own lived experience. Situated within the context of a Hong Kong Secondary School English Panel Chairperson (Department Head) implementing an AI-assisted marking policy, the paper documents the recursive dialogue between human practitioner and machine interlocutor. An analysis of the generative text artifacts produced during these sessions, the study demonstrates how the AI partner challenges the practitioner’s initial narratives. The reflection shifted the framing of hesitation from “technophobia” to a protective “Duty of Care”, and redefined a flexible adoption timeline as “Strategic Patience”. Ultimately, this paper argues that AI-Human Duoethnography offers a scalable, rigorous, yet highly accessible framework for school leaders to break the reflective echo chamber, achieving actual critical distance, generating dynamic “Third Voice” insights into educational reform.

Authors:
Sing Tsun Derek Wong, Independent Scholar, Hong Kong

---

Conference Comments & Feedback

Place a comment using your LinkedIn profile

Comments

Share on activity feed

Post with LinkedIn

Powered by WP LinkPress

Share this Presentation

Share this...

Facebook

Pinterest

Twitter

Linkedin

Posted by James Alexander Gordon

Last updated: 2023-02-23 23:45:00

All Posts