The differentiation of contemporary approaches to qualitative data analysis can seem daunting even for experienced social science researchers. Especially when they move forward in the data analysis process from general analytical strategies used in qualitative research to more specific approaches for different types of qualitative data, including interviews, text, audio, images, videos, and so-called virtual data, by discovering the domain ontology of the qualitative research field, we see that there are more than twice as many different classes of data analysis methods as qualitative research methods. This article critically reflects on qualitative research and the qualitative computer data analysis process, emphasising its significance in harnessing digital opportunities and shaping collaborative work. Using our extensive analytical and research project experience, the last research results, and a literature review, we try to show the impact of new technologies and digital possibilities on our thinking. We also try to do the qualitative data analysis. The essence of this procedure is a dialectical interplay between the new world of digital technology and the classic methodology. The use of digital possibilities in qualitative research practices shapes the researcher’s identity and their analytical and research workshop. Moreover, it teaches collaborative thinking and teamwork and fosters the development of new analytical, digital, and Information Technology (IT) skills. Imagining contemporary qualitative research and data analysis in the humanities and social sciences is difficult. Opening to modern technologies in computer-based qualitative data analysis shapes our interpretation frameworks and changes the optics and perception of research problems.

The integration of artificial intelligence (AI) into qualitative research is shifting the boundaries of knowledge production. Instead of mere tools, AI is increasingly conceptualized as a co-researcher or research partner—an epistemic agent that helps in the analysis, interpretation, and reflexivity of data. This article proposes an abductive model, called AbductivAI, for analyzing qualitative data, based on collaboration between humans and AI agents. It explores how AI functions as an analytical partner, reflexive collaborator, and participant in distributed cognition within the framework of the human-AI qualitative research workflow and data analysis approach. To build the model, we used advanced prompting techniques (Chain-of-Prompting) and used the 323 abstracts submitted to the World Conference on Qualitative Research as data. Although this data was not intended for research, its exploration allowed us to test the AbductivAI model, showing that interactions between humans and AI agents can benefit qualitative data analysis if the human recognizes and defines their role as a reflexive agent responsible for the entire process.

Qualitative Research has gained greater scientific recognition in recent years, given the improvement of denser methodologies, more backed by knowledge from different areas of expertise. One of the factors that contributed to improving the quality of qualitative studies was incorporating a set of tools, most of them digital. Although these tools can support and reduce subjectivity, they must be aligned with the theoretical-conceptual-methodological frameworks of the research, which are the axes that give it coherence and cohesion. The researcher’s skills are fundamental to guarantee the integrity and ethics of the research construction process, from project formulation to disseminating results. In this context, in the case of qualitative data analysis, tools based on Artificial Intelligence (AI) can help researchers identify patterns and trends through a large volume of data, generate visualisations and syntheses and even offer suggestions for other questions or research areas. On the other hand, this dimension implies that the researcher develops digital and multimodal literacies. This essay is expected to discuss the challenges to overcome in developing and using accurate tools.