Methods for Dialoguing with the Algorithm: Autoethnography

This is the first section, Autoethnography, of the Methods Chapter for: Dialoguing with the Algorithm: An Autoethnographic Study of Midlife Voice, Uncertainty, and Teacher Identity in a ChatGPT Exchange.

by: Maria Lisak EdD (How to cite)

Autoethnography

Overview of Approach

This study adopts a layered autoethnographic methodology, drawing on a single stream of reflective material—AI dialogues, pedagogical notes, analytic journaling, and field-level observation—developed through sustained engagement over time. Each of the three inquiries in this research program (Lisak, 2025a, 2025b, and the current study) re-examines the same base material through a different conceptual lens, allowing key themes to surface, settle, and evolve across repeated analytic passes.

The first inquiry, Designing with AI: Reflective Inquiry (Lisak, 2025a), framed the dataset within practitioner research, focusing on the pedagogical design implications of AI use in EFL contexts. The second, Designing with AI in Precarious Times (Lisak, 2025b), re-read the same material through the lens of labor, precarity, and emotional endurance, reframing uncertainty as a methodological resource. This third study continues the recursive process, centering identity, relational labor, and pedagogical ethics in later life. I treat these three projects not as discrete studies but as linked phases in an evolving inquiry, in which AI functions as a discursive partner, and where identity, pedagogy, and ethics remain in continuous negotiation.

This approach is situated in dialogic autoethnography and reflective practitioner research, but with an emphasis on epistemic accountability: meaning-making is tracked over time and across engagements, revealing shifts in stance and voice through the ongoing relationship between researcher, technology, and context.

Table of Recursive Analysis across three studies:

Study

Analytical Lens

Key Focus

Primary Insights

Designing with AI (2025a)

Practitioner inquiry

Ethical AI integration

How to experiment with AI without abandoning care, context, reflection

Designing with AI in Precarious Times (2025b)

Theoretical/affective

Emotional labor, precarity

Temporal fatigue, institutional invisibility, "precarity-as-method"

Current study

Identity-focused

Professional becoming

Five moments of identity negotiation, "conscious cultural mediation"


Autoethnographic Orientation

Autoethnography positions the self as a site of layered meaning-making in relation to broader cultural and technological systems (Adams, Holman Jones, & Ellis, 2015; Ellis, Adams, & Bochner, 2011). Rather than memoir, it is the systematic analysis of personal experience to illuminate cultural processes. This project draws on poststructural and critical autoethnography (Boylorn & Orbe, 2020; Gannon, 2006), where uncertainty, fragmentation, and multiplicity are recognized as legitimate analytic modes.

Autoethnography is particularly suited to AI-mediated encounters, where identity is dialogically constituted. Following Bakhtin’s (2010) concept of heteroglossia, I treat my exchanges with ChatGPT as multi-voiced interactions in which professional, personal, and algorithmic voices intermingle. These conversations are not neutral—they reflect, refract, and sometimes distort my prompts, creating a dynamic site for identity negotiation.

This orientation enables the central analytic task of this study: identifying critical incidents (Tripp, 1993) where my professional identity was unsettled, affirmed, or reconstituted. Such incidents are read not merely as pedagogical tensions, but as moments of ontological transformation, where the teacher-self emerges through intra-action with the algorithm (Barad, 2007).

Ethical Considerations

Ethical practice in this work involved sustained reflexivity, transparency in representing both human and AI contributions, and acknowledgment of the recursive authorship that arises when outputs are shaped through human-AI collaboration.

For this paper, click here. 

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