Serendipity, Reflexive Uncertainty, and AI Dialogue in Qualitative Research
António Pedro Costa, Faculty of Psychology and Educational Sciences, University of Porto (Portugal)
António Pedro Costa is a researcher at the Centre for Research and Intervention in Education (CIIE), Faculty of Psychology and Educational Sciences, University of Porto, Portugal. He is one of the researchers behind the qualitative data analysis software webQDA (webqda.net) and teaches research methodology courses. He coordinates the Ibero-American Congress on Research Methods (ciaiq.ludomedia.org) and the World Conference on Qualitative Research (wcqr.ludomedia.org), and serves as Editor-in-Chief of the journal New Trends in Qualitative Research (NTQR). His research focuses on the use of technologies in research, with particular emphasis on Artificial Intelligence, research ethics, mixed methods, and data curation. In 2023, he received an Honourable Mention in the University of Aveiro Researcher Award and has coordinated publicly and privately funded research projects worth more than €3 million.
Qualitative research has often been characterized by a paradox: while it claims to be open to complexity, ambiguity, and the emergence of social phenomena, it frequently ends up reproducing predictable, incremental, and overly controlled analytical trajectories. During the workshop “Serendipity favors the prepared mind – and vice-versa”, held at the 10th World Conference on Qualitative Research (wcqr.ludomedia.org) in Madrid on 21 January 2026, Mats Alvesson argued that much of contemporary research suffers from a lack of substantive novelty, favouring safe and cumulative contributions over potentially transformative discoveries. In this context, serendipity emerges as a key concept for rethinking the production of qualitative knowledge.
Serendipity does not refer to pure chance or to fortuitous discovery devoid of rigor. On the contrary, it concerns the capacity to recognize and value unexpected observations, framing them as relevant analytical opportunities. Cunha et al. (2015) define serendipity as unexpected observations rendered actionable through a frame of reference that remains dynamic by cultivating doubt and valuing “not-knowing” as an epistemological stimulus. Serendipity thus presupposes a “prepared mind,” capable of suspending assumptions and reconfiguring research problems in light of what emerges in the field.
Research is therefore often a succession of missed opportunities. The literature draws attention to the fact that unexpected triggers — surprising, disturbing, or inspiring observations — tend to be neglected or marginalized outside the specialized literature on serendipity. Yet it is precisely these triggers that can reveal dimensions of the phenomenon that escape initial theoretical frameworks and overly rigid methodological strategies.
In qualitative research, such triggers can take multiple forms: an unexpected response in an interview, a contradiction in the data, a significant silence, or even the researcher’s own interpretive discomfort. When reflexively embraced, these moments do not represent failures of the research design but rather analytical turning points that enable the production of new and relevant knowledge.
Serendipity thus constitutes a fundamental dimension of high-quality qualitative research: it does not replace methodological rigor but redefines its meaning, shifting it from excessive control toward sensitive attention to the emergent.
The importance of serendipity thus lies in its potential to challenge the logic of confirmation and linearity that frequently dominates scientific practice. Rather than treating the unexpected as noise, serendipity-oriented qualitative research recognizes it as a signal. Such a stance demands continuous reflexivity regarding the researcher’s own theoretical, methodological, and ontological frameworks, avoiding both the romanticizing of chance and the premature neutralization of surprise.
Serendipity thus constitutes a fundamental dimension of high-quality qualitative research: it does not replace methodological rigor but redefines its meaning, shifting it from excessive control toward sensitive attention to the emergent. This redefinition, however, is not spontaneous — it depends on a systematic reflexive stance toward uncertainty.
Reflexive uncertainty (Costa & Bem-Haja, 2025) operates as an epistemological condition that keeps the researcher attentive to the limits of their theoretical, methodological, and interpretive frameworks, avoiding the premature closure of meaning. Operationalized in the Reflexive Uncertainty Framework (RUF), this reflexivity documents analytical uncertainty across three moments: the production of probabilistic classifications, the detection of zones of interpretive ambiguity, and the researcher’s reflexive commentary as co-analyst. It is on this terrain that serendipity becomes possible and productive.
Unexpected moments, such as displaced responses, trivial observations, or seemingly obvious statements, function as analytical triggers. These triggers only acquire epistemological value when the researcher does not treat them as noise or deviation but as signals that the phenomenon under study may be poorly framed or under-theorized. Serendipity, thus, does not emerge from chance itself but from the reflexive capacity to reinterpret the unexpected.
In this sense, reflexive uncertainty (Costa & Bem-Haja, 2025) precedes and sustains serendipity. By recognizing that the knowledge produced is always partial, situated, and provisional, the researcher creates space for surprising observations not to be neutralized by rigid analytical categories. This reflexivity is necessary for unexpected moments to produce relevant theoretical contributions.
It is within this framework that dialogue with artificial intelligence systems, specifically Large Language Models (LLMs), can be understood as a reflexive methodological device rather than merely an instrumental tool. The AbductivAI model (Costa et al., 2025), grounded in Actor-Network Theory and sociomateriality, proposes exactly this shift in status: AI ceases to be a mere tool and instead acts as a co-researcher, reflexive collaborator, and participant in distributed cognition throughout the analytical process. When used dialogically, AI can:
- question the researcher’s implicit assumptions;
- propose alternative readings of the data;
- reveal interpretive incoherences, tensions, or silences;
- amplify productive estrangement in relation to the empirical material.
In this way, AI can function as a “reflexive other,” helping to sustain uncertainty as an epistemological resource rather than a problem to be eliminated. This is what RUF (Costa & Bem-Haja, 2025) seeks to make visible when applied to AbductivAI’s Chain-of-Thought Review Loop phase: when a data excerpt receives close scores between competing categories, the ambiguity is not automatically resolved but returned to the researcher as an explicit interpretive trigger. By engaging in dialogue with AI about qualitative data, provisional interpretations, or emerging categories, the researcher can create conditions for new “serendipitous triggers” to arise—not as automatic responses, but as interpretive shifts that demand human judgment.
It should be emphasized that AI does not produce serendipity autonomously. As in classic examples from qualitative research, serendipity continues to depend on the researcher’s theoretical sensitivity, empirical experience, and reflexivity. AI, in this context, acts as a mediator of reflexive dialogue, helping to sustain the analytical openness necessary for the unexpected to be recognized as insight rather than dismissed as error.
In sum, the articulation between serendipity, reflexive uncertainty, and AI dialogue allows qualitative research to be rethought as a less linear process, more responsive to the emergent. Far from compromising rigor, this approach redefines it as the capacity to inhabit uncertainty productively, transforming empirical surprises into meaningful theoretical contributions. In an academic context marked by pressures for productivity and predictability, recovering serendipity means reaffirming the value of curiosity, doubt, and openness as essential conditions for producing socially and theoretically relevant knowledge.
References
Alvesson, M., & Gjerde, S. (2013). Serendipity in theory development? The significance of unexpected events in the development of organizational theory. Organization Studies, 34(3), 367–385. https://doi.org/10.1177/0170840612466622
Costa, A. P., & Bem-Haja, P. (2025). Reflexive Uncertainty AI for Qualitative Data Analysis. In E. Marengo, M. Strian, & M. IPonticorvo (Eds.), Proceedings of the 2nd International Workshop on Education for Artificial Intelligence (edu4AI2025) (pp. 13–23). European Association for Artificial Intelligence (EurAI). https://ceur-ws.org/Vol-4114/7_paper.pdf
Costa, A. P., Bryda, G., Christou, P. A., & Kasperiuniene, J. (2025). AI as a co-researcher in the qualitative research workflow: Transforming human-AI collaboration. International Journal of Qualitative Methods, 24, 1–12. https://doi.org/10.1177/16094069251383739
Cunha, M. P., Clegg, S. R., Rego, A., & Neves, P. (2015). Serendipity in the field: Triggering events and the dynamics of discovery. Organization Studies, 36(9), 1201–1227. https://doi.org/10.1177/0170840615588903
