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Artificial Intelligence in Qualitative Research: Insights From Experts via Reflexive Thematic Analysis
Federica Dellafiore1, Andreina Saba2, Concetta Collaro3
1Department of Life Health Sciences and Health Professions, Link Campus University, Roma, Italy.
Qualitative Health Research
|December 6, 2025
Summary
Artificial intelligence (AI) offers efficiency gains in qualitative research, but experts urge caution. Human oversight and ethical frameworks are crucial for integrating AI while preserving the core of qualitative inquiry.
Area of Science:
- Qualitative Research Methods
- Artificial Intelligence in Academia
- Socio-anthropological Studies
- Healthcare Research
Background:
- The integration of artificial intelligence (AI) into qualitative research is a growing area of interest but faces significant controversy.
- Concerns include epistemological differences, ethical considerations, and the human-centered nature of qualitative inquiry.
- Expert perspectives are vital for understanding the implications of AI in this field.
Purpose of the Study:
- To explore the perspectives of expert qualitative researchers on the opportunities, challenges, and future directions of AI in qualitative inquiry.
- To investigate attitudes towards AI integration among socio-anthropological and healthcare researchers in Italian academic and hospital settings.
- To identify key themes related to the use of AI in qualitative research, including ethical and practical considerations.
Main Methods:
- Semi-structured interviews were conducted with 14 expert qualitative researchers.
- Reflexive thematic analysis was employed to analyze the interview data.
- The study focused on researchers in Italian academic and hospital settings within socio-anthropological and healthcare fields.
Main Results:
- Researchers expressed ambivalent attitudes towards AI, balancing curiosity with technophobia and emphasizing the need for human oversight.
- A strong emphasis was placed on reflexivity, creativity, and researcher identity as crucial counterbalances to AI's mechanistic tendencies.
- While AI is seen as beneficial for transcription and data management, skepticism remains regarding its capacity for complex interpretation. Ethical concerns, including bias and data privacy, were also highlighted.
Conclusions:
- AI presents opportunities to enhance efficiency and accessibility in qualitative research, but human interpretative agency must remain central.
- Developing robust ethical frameworks, fostering critical reflexivity, and adopting innovative conceptual approaches are essential for responsible AI integration.
- A balanced approach is advocated to leverage AI's potential while safeguarding the human core of qualitative research and education.
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