What do humans have to do with qualitative research ,and what can human researchers offer to qualitative research that AI cannot?

• In Event: Complicated and Complicating Questions About the Role of AI in Qualitative Inquiry

Abstract

As a social work qualitative researcher, I often approach the research process through a relational lens, focusing on connections that foster trust, mutual understanding, and ethical engagement (Cleece et al., 2025). This lens relies on human researchers’ capacity for empathic attunement, negotiating power dynamics, and interpreting rich narratives within sociocultural contexts. Human researchers also bring creative insight and imaginative synthesis of observations to generate interpretations beyond the data. While artificial intelligence (AI) offers speed and efficiency in coding and synthesis, it cannot replicate the reflexive, ethically grounded relationships human researchers forge with participants (McNamee, 2014).

Rooted in a social constructivist worldview, this relational work focuses on the qualitative principles of trustworthiness, reflexivity, and co construction of knowledge (Berger & Luckmann, 1966; Lincoln & Guba, 1985). Knowledge is created collaboratively. Researchers draw on creative interpretation and intuition to generate insights grounded in participants lived experiences. Reflexivity shapes interaction in an intersubjective process, as the researcher’s background, assumptions, and emotions influence the dialogue, and participants’ responses shape the research stance (Finlay, 2002). Reflexive co construction of meaning supports credibility, dependability, and confirmability, which are core aspects of trustworthiness (Shenton, 2004). This reflexive engagement rests on human judgement, empathy, and moral awareness, qualities that current AI tools struggle to emulate.

At its heart, relational practice also involves building genuine trust and rapport, so community partners feel safe sharing their lived experiences (Guillemin & Heggen, 2009). Qualitative researchers attend not only to what is said, but how it is said, including the tone, body language, and pauses, while in turn responding with empathy to foster rich and collaborative data (Lavee & Itzchakov, 2021). AI tools can track pause lengths or word frequencies, but they cannot interpret the emotional significance of a hesitated word or provide empathic attunement. Prior (2017) further emphasizes that empathic attunement often emerges in the subtle shifts of tone or pacing that follow a brief pause, signaling to participants that their experiences are both heard and valued. These examples highlight AI’s limitations in discerning relational nuances and emergent meanings during data collection and analysis.

Finally, relational ethics calls for ongoing ethical attentiveness. Guillemin and Gillam (2004) highlight that researchers must make real time moral judgments around consent, confidentiality, and participant well being when unexpected issues arise to safeguard participant welfare. AI tools cannot empathize or exercise moral reasoning when confronted with unanticipated ethical dilemmas (Bradbury & Lichtenstein, 2000). Only human researchers can navigate these complex moral landscapes and ensure ethical respect at every turn. Such complex moral reasoning remains beyond the reach of AI.

As AI tools become more prevalent in qualitative research, this is not a call to abandon technological advances. This is a reminder to weave relational practices into every stage of qualitative inquiry. Trust building, reflexivity, co construction of knowledge, empathic attunement, and ethical attentiveness are human capacities that AI cannot replace. By integrating these practices with AI, we can benefit from technological efficiency while preserving the deep, human centered and relational insights that define rigorous qualitative inquiry.

Author

• Johanna Creswell Báez, University of Colorado - Colorado Springs