autoethnography
Filtering by topic autoethnography(5)Clear all filters
- PaperTESOL Quarterly12 Jul 2026
Language Teacher Educator Identity, Wellbeing and Agency Nested in a Community of Practice: A Collaborative Autoethnography
Yasemin Tezgiden‐Cakcak, Aycan Demir‐Ayaz, Işıl Günseli Kaçar
Three language teacher educators in Türkiye used collaborative autoethnography to examine how their community of practice (CoP) helped them navigate identity tensions, boost wellbeing, and enact agency. The CoP provided a collegial space that supported professional empowerment and resilience within neoliberal academia. Findings reconceptualize identity, wellbeing, and agency as interdependent processes nested in a CoP.
Original abstract
In this collaborative autoethnography, we, as three university‐based language teacher educators in Türkiye, explore how our community of practice (CoP) acted as a catalyst in navigating our identity tensions, boosting our wellbeing and contributing to our agency. Grounded in Wenger's (2009) framework of CoP, we embarked on a critical journey of navigating the tensions between our aspired and assigned identities as teacher educators in a dialogic and reflective third space of a CoP. The collegial space of our CoP enabled us to enact our agency and nurtured our wellbeing, embracing us in a safety net of solidarity and empowering us professionally in neoliberal academia. Conceptualizing the identity–wellbeing–agency triangle as a mutual, recursive and interdependent process nested in a CoP, this study showcases how LTEs reclaim their legitimacy, nourish their wellbeing and enact their agency in a safe dialogic space. Such a reconceptualization may bring new insights to LTEs in different contexts.
- PaperJournal of Second Language Writing9 Jul 2026
Toward an ecological model of L2 writing teachers’ emotion labor: A poetic autoethnography
Shizhou Yang
Uses poetic autoethnography to propose an ecological model of emotion labor among second language writing teachers.
- PaperTESOL Journal8 Jul 2026
Refusing the Feeling Rules: Arts‐Based Autoethnography as Emotional Resistance in Language Teacher Education
Jialei Jiang, Wenxi Schwab
This study uses arts-based autoethnography to explore the emotional labor of a raciolinguicized TESOL educator navigating systemic inequities in a US post-secondary program. Digital artworks and narrative reflections reveal how emotions like shame and anger, shaped by native-speakerism and institutional feeling rules, can be mobilized as critical affective literacy. Findings indicate arts-based inquiry provides a pedagogical approach for TESOL educators to challenge systemic injustice and foster equitable pedagogies.
Original abstract
This study employs arts‐based autoethnography to examine the emotional labor of Wenxi, a raciolinguicized TESOL educator who navigates systemic inequities in a predominantly white post‐secondary education program in the United States. Drawing on digital artworks and narrative reflections created through an arts‐based workshop, the study explores how emotions such as shame, anger, and disheartenment can be mobilized as forms of critical affective literacy. Wenxi's narratives reveal how experiences of native‐speakerism, racial marginalization, power inequality, and institutional feeling rules have shaped her professional identity and emotional struggles. Yet, through art making, these emotions are reframed as resources for critical resistance and reflection. The findings suggest that arts‐based inquiry offers a robust pedagogical approach in teacher education by creating collective spaces where TESOL educators navigate and challenge systemic injustice. By fusing critical affective literacy with arts‐based practices, this study demonstrates the potential of emotional resistance to disrupt normative professional discourses and to foster more equitable, justice‐oriented TESOL pedagogies.
- PaperTESOL Journal6 Jul 2026
Critical Autoethnography With Multiple Large Language Models: AI ‐Stimulated Reflexive Practice on Language Teacher Educator Identity
David Gerlach
Large language models (LLMs) served as reflective and dialogical partners in critical autoethnographic narrative research on language teacher educator identity. Three LLMs generated probing questions and thematic outputs that stimulated the researcher's reflexive interpretation, while meaning-making and theoretical synthesis remained human practices. The methodological approach revealed patterns of consciousness-raising, institutional precarity, pedagogical transformation, and relational critical identity construction.
Original abstract
This study proposes a methodological approach in which large language models (LLMs) serve as reflective and dialogical partners rather than analysts in critical autoethnographic narrative (CAN) research on language teacher educator identity. During an ongoing debate about the use of generative artificial intelligence (AI) in reflexive qualitative research, this approach occupies a narrow methodological space. LLM outputs serve as stimuli for the researcher's reflexive interpretation of CAN texts, while meaning‐making, salience judgment, and theoretical synthesis remain human practices. In a two‐phase design, three LLMs (ChatGPT 5.2 Thinking, Claude 4.5 Sonnet, and Gemini 3 Thinking) first generated reflective stimuli in the form of probing questions and then produced parallel thematic outputs that served only as material for the researcher's reflexive synthesis, not as completed analyses. Four patterns emerged: consciousness‐raising through dialogical awakening, institutional precarity and resistance, pedagogical transformation, and the relational construction of critical identity. The LLMs differed in orientation in ways consistent with poststructuralist epistemology. The contribution is primarily methodological: a procedure for AI‐stimulated reflexive practice in language teacher education research, alongside a critical engagement with the ethical, environmental, and epistemic costs such practice entails.
- PaperTESOL Journal2 Jun 2026
“I Wanted to Show I Could Do This on My Own”: Vulnerability and Professional Becoming in the Age of ChatGPT
Amr Rabie‐Ahmed, Curtis Green‐Eneix
This collaborative autoethnography explores how two novice multilingual educators experience professional vulnerability and identity (re)formation at the intersection of AI, emotion, and institutional expectations. Using reflection logs, identity maps, and collaborative dialogues, the authors illustrate trajectories of resistance, adaptation, and ethical negotiation.
Original abstract
The rise of generative artificial intelligence (GenAI), particularly OpenAI's ChatGPT, has sparked global debate in education regarding authorship, ethics, and knowledge production. While much research highlights AI's affordances and risks, less attention has been paid to how early‐career multilingual teacher educators navigate this terrain. This collaborative autoethnography explores how two novice multilingual educators, working in transnational contexts, experience professional vulnerability and identity (re)formation at the intersection of AI, emotion, and institutional expectations. Drawing on a new materialist stance, we trace how our orientations to AI are entangled with emotions, accountability, and language norms. Using reflection logs, identity maps, and collaborative dialogues, we illustrate complementary trajectories of resistance, adaptation, and ethical negotiation. This study contributes to research on multilingual teacher educator development by foregrounding emotional reflexivity, techno‐ethical tensions, and identity assemblage in the age of AI.