2026· Journal of Edugeography· Vol 7, pp. 331-345· 0 citations
TL;DR
An autoethnographic experiment was described in which three authors used AI to mimic their writing voices and to compare their unassisted responses to a research question about the nature and meaning of autoethnographic writing, revealing startling insights about the nature of writing identity and the human search for meaning in the age of AI.
Abstract
Although AI is most often framed as a cognitive tool, its social and psychological effects are arguably much more profound. AI has far-reaching implications for human experience on an emotive, embodied level; it is not simply another technical innovation, since its rapid evolution will likely change our deepest, most cherished beliefs about what it means to be human and how we see ourselves, collectively and individually. It raises unique challenges for those who are invested in forms of creative expression, especially deeply personal, intimate, and introspective writing. Accordingly, this essay describes an autoethnographic experiment in which three authors used AI to mimic their writing voices and to compare their unassisted responses to a research question about the nature and meaning of autoethnographic writing. Results revealed startling insights about the nature of writing identity and the human search for meaning in the age of AI, causing the authors to question just what makes us quintessentially human anyway.
In a world of deepfakes, generative AI, and ongoing debates over cultural appropriation, questions about what makes art “authentic” are newly urgent. We build on work from science and the humanities to offer a unified psychological account of perceived
art authenticity
. Across six studies, we find that people see art as more authentic when it appears to reveal a mind—a creator's lived experience, emotions, and intentions. The importance of “revealed mind” helps clarify central debates surrounding authenticity, explaining why replicas can sometimes feel authentic, and why audiences celebrate art that reflects an artist's lived experience and grow outraged when an artist tells someone else's story. Perceptions of revealed mind are especially important in the age of AI, explaining why AI-generated art feels inauthentic even when technically impressive. Together, our findings suggest that art feels most authentic when it evokes a sense of connection to the artist's mind.
Danica Dillion, Neil Hester, Yochanan E. Bigman et al.· Empirical Studies of the Art...· 0 citations
The work argues that autoethnography functions not merely as a qualitative method but as a phenomenological mode of being and knowing that safeguards the primacy of anthropic subjectivity against machinic imitation.
This article examines how conversational artificial intelligence (AI) can function as a catalyst for identity awareness and reflexive sensemaking among women navigating emotionally demanding life roles.
Using analytic autoethnography, the study draws on sustained AI-mediated dialogues alongside reflexive writing to examine how meaning-making unfolded during a period of personal and professional tension. The analysis is informed by emotional labour theory, transformative learning and feminist standpoint perspectives.
The findings illustrate how AI-mediated dialogue supported the articulation of previously silenced emotions and facilitated recognition of internalised obedience, inherited scripts of “good daughterhood” and gendered expectations shaping everyday life. Rather than producing solutions, AI functioned as a narrative scaffold that enabled reflexive distancing and identity re-seeing.
This article contributes to qualitative scholarship by offering an autoethnographic account of AI as a reflective companion rather than a therapeutic or authoritative agent. It highlights how women, particularly those balancing motherhood and academic identities, may use AI-mediated dialogue to surface emotional labour and renegotiate their sense of self.
N. A. Zulkifly· Qualitative Research Journal· 0 citations
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.
This paper investigates a Chinese New Zealander, immigrant, and arts-based researcher’s evolving sense of belonging through autoethnography and arts-based inquiry. Building on insights from the author’s previous critical autoethnographic studies of identity transformation, this work seeks to communicate complex experiences in new, resonant ways, turning to AI-assisted songwriting as a means to delve deeper into the question of belonging. AI-assisted songwriting enables the researcher to confront the traditional boundaries of authorship and explore how identity can be expressed and interpreted through collaboration with a non-human entity. This partnership allows the author to take on multiple roles, as composer, performer, and listener, mirroring the complexity of an evolving sense of self and challenging conventional notions of ownership in creative work. Rather than replacing the embodied engagement of autoethnography, this study invites a rethinking of practice in the AI age. This creative collaboration provokes reflection on key questions: Can AI authentically reflect the deeply personal experiences central to the narrative of belonging? What ethical implications arise when AI technology is used in critical self-inquiry? This paper contributes to the growing discourse on generative AI and autoethnography by offering insights into how AI can be both a tool for creative expression and a catalyst for deeper self-reflection. By embracing both the possibilities and challenges that AI presents, this paper intends to expand the boundaries of critical autoethnography and invites others to consider how AI technology can be integrated into their own research practices with care, intentions, and reflexivity.
It is argued that AI-driven “style replication” is fundamentally a negotiation of social relations, one that recentres platform power and reorganises the conditions of human-AI collaboration.
Meng Liang, Fangzhou Zhang· Media, Culture & Society· 1 citation