Collaborative Autoethnographic Research

• Collaborative autoethnographic methodology is a qualitative approach that integrates personal narratives with group dialogue to enhance research depth and credibility.
• It employs structured reflective documentation, thematic coding, and iterative co-validation to mitigate individual subjectivity and generate multi-layered insights.
• Its applications across education, HCI, and social computing promote equitable, co-created knowledge that addresses power dynamics and epistemic justice.

Collaborative autoethnographic methodology is an approach in qualitative research that centers on the systematic, critical, and reflexive exploration of personal experience through the collaboration of multiple researchers or between researcher and facilitator. In contrast to traditional, solitary autoethnography, the collaborative variant leverages dialogue, multi-perspective analysis, and shared interpretive frameworks to increase rigor, depth, and contextual validity. Collaborative autoethnography has been deployed as both a primary method and as an augmentation to structured qualitative methods across domains including education, human-computer interaction (HCI), design, social computing, and technology integration. This methodology foregrounds both subjectivity and social context, producing insights into identity formation, supervision, power, and the mediation of knowledge in differentiated professional and technological environments.

1. Foundational Principles and Motivations

Collaborative autoethnography is anchored in the marriage of two axes: the insider knowledge and lived experience of research participants (often researchers themselves), and the external, dialogic scaffolding provided by other collaborators—whether facilitators, disciplinary specialists, or peers (Cornell et al., 2020, Ajmani et al., 3 Jul 2024). The key advantage is its capacity to move beyond the idiosyncratic tendencies of individual autoethnography by systematically surfacing and negotiating multiple perspectives.

Motivations for adopting a collaborative autoethnographic approach include:

• Mitigating subjectivity by integrating cross-validation through dialogic reflection.
• Enabling the emergence of nuanced, multi-layered thematic patterns, especially in research addressing complex phenomena such as digital identity construction, epistemic (in)justice, or the mediation of critical literacies in technology (Gupta et al., 15 Jan 2024, Ajmani et al., 3 Jul 2024).
• Responding to ethical imperatives for co-authorship, agency, and epistemic autonomy on the part of marginalized or directly affected populations (Ajmani et al., 24 Jan 2025).
• Enhancing the reflexivity and actionability of findings by blending practitioner and analytic perspectives (Cornell et al., 2020, Russo et al., 25 Aug 2024).

2. Methodological Structures and Processes

Implementation of collaborative autoethnography varies in granularity and structure but generally encompasses the following components:

• Facilitated narrative documentation: One or more participants articulate their lived journey (e.g., professional development, technology adoption) in conversation with a facilitator who probes latent themes and pushes beyond anecdote (Cornell et al., 2020).
• Synchronous/asynchronous collaborative reflection: Researchers maintain individual logs, diaries, or structured reflections, later comparing and synthesizing these accounts through discussion, iterative coding, or group workshops (Gupta et al., 15 Jan 2024, Li et al., 21 Feb 2024).
• Thematic analysis and coding: Qualitative analysis is performed collaboratively, using open and axial coding—a process in which themes are iteratively refined until thematic saturation is reached. Tools such as Atlas.ti or similar qualitative data platforms are used as organizational infrastructure (Li et al., 21 Feb 2024). Analysis may be formalized:

$$\textbf{Themes} = f(\text{Reflective Narratives}, \text{Artefacts})$$

• Structured frameworks and evaluative models: Thematic analysis is mapped onto domain-specific theoretical constructs, such as Legitimation Code Theory (LCT) in disciplinary identity formation (Cornell et al., 2020), or onto analytical grids (e.g., plotting metaphors by anthropomorphism and literacy) (Gupta et al., 15 Jan 2024).
• Iterative co-validation: Emergent insights and interpretations are subjected to ongoing group review, member checking, or iterative feedback sessions to maximize the soundness and trustworthiness of results (Cornell et al., 2020, Ajmani et al., 24 Jan 2025).

3. Theoretical and Analytical Frameworks

Collaborative autoethnographic studies typically operationalize analysis via established theoretical models, ensuring systematic rigor and facilitating generalizability:

• Legitimation Code Theory (LCT): In educational contexts, disciplinary identity is examined through the Specialisation dimension, quantifying epistemic (ER) and social relations (SR) as axes, and classifying knowledge and knower codes:

$$\text{Knowledge Code:} \quad ER^{+},\; SR^{-}\qquad \text{Elite Code:} \quad ER^{+},\; SR^{+}$$

(Cornell et al., 2020).

• Multiliteracies and Critical Literacy: The structure of personal reflections is mapped against literacy frameworks (functional–critical–rhetorical) and cross-referenced against dimensions such as anthropomorphism when studying public perceptions of AI (Gupta et al., 15 Jan 2024).
• Meta-cognitive and Decision Models: When evaluating digital tool integration (e.g., ChatGPT in thesis writing), multicriteria decision analysis is formalized:

$$\begin{aligned} I &= \text{rating for interactivity (1–3)} \ F &= \text{rating for feedback (1–3)} \ \bar{R} &= \left\lceil \frac{I + F}{2} \right\rceil \ S &= 4 - \bar{R} \end{aligned}$$

(Schwenke et al., 2023).

• Epistemic Autonomy Ratio: Centering participant knowledge with minimized external control is conceptualized by the ratio:

$EA = \frac{K_p}{C_e}$

where $EA$ is epistemic autonomy, $K_p$ is participant knowledge, and $C_e$ is external control (Ajmani et al., 24 Jan 2025).

4. Applications Across Domains

Collaborative autoethnographic methodology is deployed in various disciplinary contexts:

• Education and professional identity: Analysis of postgraduate supervision and identity development of theoretical physicists using facilitated reflective interviews and LCT (Cornell et al., 2020).
• Critical technology literacy: Mapping metaphorical framings of AI with structured group reflection, coordinating thematic findings via multiliteracies and ethical frameworks (Gupta et al., 15 Jan 2024).
• Human-computer interaction and design: Reflexive co-design practices, involving multi-role (designer/researcher/facilitator) analysis of virtual participatory workshops (Chivukula et al., 2022).
• Digital well-being and technology adoption: First-person and collaborative reflections on the mediation of presence and engagement by everyday digital devices, with subsequent design implications for augmented reality (Tran, 4 Mar 2025).
• Social computing and epistemic justice: Multi-participant hybrid autoethnographies surface testimonial and hermeneutical epistemic injustices within online transgender healthcare, LGBTQ+ communities, and the curation of Indigenous knowledge (Ajmani et al., 3 Jul 2024).
• Bridging research and practice: Reflective group autoethnographies examine interactions with practitioners to reveal tensions between academic and real-world timelines, knowledge, and values (Russo et al., 25 Aug 2024).
• Centering marginalized voices: Participant-driven co-creation and iterative member checking foreground epistemic autonomy, especially for marginalized communities within HCI (Ajmani et al., 24 Jan 2025).

5. Benefits, Limitations, and Methodological Rigor

Strengths:

• Richness and deep contextualization of findings via the juxtaposition of insider and outsider perspectives (Cornell et al., 2020).
• Enhanced trustworthiness and credibility through iterative group synthesis and external validation (Cornell et al., 2020, Li et al., 21 Feb 2024).
• Robust surfacing of power dynamics, epistemic injustice, and systemic marginalizations that may otherwise be rendered invisible or misinterpreted (Ajmani et al., 3 Jul 2024, Ajmani et al., 24 Jan 2025).

Limitations:

• Risk of subjectivity in synthesis; the process is heavily reliant on collaborative reflexivity and transparency.
• Methodologically labor-intensive, often requiring extended cycles of coding, re-coding, and discussion to reach thematic saturation (Li et al., 21 Feb 2024).
• Potential difficulty in transferring findings beyond local or self-selected groups unless rigorously connected to theoretical frameworks.

Methodological rigor is maintained via structured prompts, iterative analysis, triangulation with external data (e.g., interviews or artefacts), and careful documentation of the analytic process (including the use of tables, LaTeX-formatted frameworks, and explicit checking for saturation).

6. Prospects, Variants, and Implications for Future Research

Collaborative autoethnographic methodology continues to evolve, with recent innovations focused on participant-centered autonomy and the remediation of epistemic injustice. The push toward epistemic autonomy reorients research as a collaborative, negotiated, and co-constructed process, particularly relevant in HCI and research with marginalized communities (Ajmani et al., 24 Jan 2025). Variants such as asynchronous remote communities and hybrid autoethnographies blending practitioner and researcher voices extend the method’s reach and inclusivity.

A plausible implication is that as digital research environments and AI systems become central to knowledge production, collaborative autoethnography will increasingly serve as an epistemic counterweight—ensuring that actor narratives, context-embedded judgments, and experiences shape future technologies, policies, and scholarship. This methodology supports the design of equitable research practices and sociotechnical systems, reinforcing the centrality of reflexivity, multimodality, and agency in qualitative inquiry.