Anthropic, the company behind the generative AI tool Claude, claimed in March 2026 that it used an AI interviewer to conduct “the largest and most multilingual qualitative study” ever done. The AI tool collected responses from nearly 81,000 people about their visions for AI, spanning 70 languages and 159 countries. Anthropic contends that tools like this can enable researchers to conduct “rich, open-ended interviews at a very large scale.”
Qualitative research is useful for understanding the lived experiences of people. “Qualitative” refers to both the type of data that researchers collect and their purpose for conducting a study. Qualitative data includes text, images, audio, video and anything that isn’t a number. This is why the term “qualitative” is often discussed in contrast to “quantitative” – that is, numerical – data.
Qualitative research enables researchers to deeply explore the tensions, ambiguities and paradoxes that characterize everyday life. It also helps unpack how social norms, cultural dynamics and subjective experiences shape people’s perspectives, beliefs and attitudes.
So, can an AI model without lived experience or a capacity to self-reflect connect with people enough to understand their worlds?
We are researchers who specialize in qualitative research on digital technologies. Collectively, we have decades of experience developing, conducting and publishing interview studies, and we teach qualitative research methods to undergraduate and graduate students.
While AI tools can support social science research, they also have significant limitations. Not taking these limitations into account risks undermining the unique value of research that relies on human connection.
What is qualitative research?
Broadly speaking, qualitative inquiry is about exploring the meaning people give to experiences.
Qualitative inquiry often involves face-to-face interviews with individuals and groups. What this looks like in practice varies based on a researcher’s academic discipline, their philosophical approach and their personal background.
While the goal is to produce explanations about the world, qualitative inquiry is designed to reveal the nuanced ways people make meaning while accounting for the different contexts that shape their experiences.
For instance, our team has used qualitative inquiry to explore how parents, children and teachers navigate digital privacy issues. We’ve also used qualitative data to analyze how influencers, activists and everyday users make sense of and respond to social media algorithms.
Anthropic Interviewer can pose questions to participants and present follow-up questions based on a participant’s response. However, we argue that qualitative inquiry requires human capacities that an AI model lacks.
AI is programmed, human conversations are not
Unlike studies focused on quantitative data, qualitative inquiry relies on flexibility.
Research that collects quantitative data requires carefully managed study conditions. They often aim to test specific hypotheses and measure the relationship between variables. To establish the validity of their findings, researchers need to demonstrate that they controlled for confounding factors.
In contrast, qualitative studies are more open-ended. They typically consider how people understand or experience the world in context. Since the world is complex, messy and nuanced, interviewers may need to change their initial questions or add new ones to collect insightful data. In other words, researchers adapt the interview to follow the conversational flow.
To plan out the interactions Anthropic Interviewer would have with study participants, researchers need to specify core interview questions and give the program instructions on how to engage with participants. For its recent study on people’s visions for AI, some of the core questions Anthropic used include “What’s the last thing you used an AI chatbot for?” and “If you could wave a magic wand, what would AI do for you?” The company did not specify what prompts or hypotheses they fed the system to come up with follow-up questions for this study.
By relying on fixed instructions, Anthropic Interviewer does not have a conversation with a participant the way a human researcher does. Instead, it executes a series of tasks in response to prompt engineering. In a conversation, a human interviewer absorbs a variety of information from a participant – their words, tone, demeanor – and responds organically in a way that meets the moment. An AI interviewer, being a machine, can act only within the parameters set by the system designers. This means that even if it is trained on large datasets, as the Anthropic Interviewer is, it will not be able to account for the unique, often unspoken relational dynamics of new interviews.
Using an AI tool can generate qualitative data, but it is not the same as conducting qualitative inquiry.
AI does not have positionality
Most qualitative researchers see their identity, lived experiences and relationships to the people they study as central to their work. This positionality can be thought of as a series of lenses through which researchers approach their studies, such as their race, gender, beliefs, values, biases and life circumstances. These factors position researchers in relation to their area of focus – as insiders, outsiders or somewhere in between, depending on the context.
Anthropic Interviewer has no position in relation to the research it is meant to support, because it has no body, identity, life history or lived experiences. Even if prompted to imitate a particular perspective – such as from “one woman to another” – it will not “contain multitudes,” as poet Walt Whitman put it, like real people do.
As opposed to a real person with a personal perspective who can genuinely respond to a live conversation, AI models use probabilities to match the patterns of how a person may commonly act or speak. It may also be alienating for participants if an AI interviewer assumes a particular persona and changes how they respond. In some ways, Anthropic AI can present only what philosopher Donna Haraway called “a view from nowhere.”
Moreover, an absence of a personal lens does not imply neutrality. Because AI systems are trained on existing data, they can reflect the dominant stereotypes and worldviews of the time, including that of their developers, curators and the companies behind them.
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The AI tool’s lack of positionality matters because this quality shapes every stage of research. This includes what questions researchers ask in interviews and how they ask them; how researchers filter information and interpret responses; and which topics they follow up on. Sharing things in common with participants – even just as a fellow human who can have firsthand experiences, thoughts and emotions – can be critical for data collection and analysis. It enables a deep, intuitive understanding of how participants perceive and interpret what they share.
A researcher’s personal lens also shapes how participants respond to them: what they choose to share and how comfortable they feel. For example, someone who grew up poor may feel more comfortable discussing debt and public assistance with someone who has a similar background than with someone who does not.
Without a personal lens, interviews can become flat and lack context. Questions may become mechanical, and the development of mutual understanding is limited. Participants may also respond differently when they sense the interviewer lacks a clear perspective.
AI cannot be reflexive
When researchers are able to reflect on their own assumptions, they can produce more thoughtful and responsible findings that avoid misrepresenting their participants. This reflexivity is another key human aspect of qualitative inquiry: researchers’ ongoing efforts to self-monitor the ways their personal background and choices over the course of a study may affect the work.
Good qualitative researchers do not try to eliminate their biases but instead try to account for them. They continually think about how their identity, experiences and perspectives shape their work and publicly share these reflections. While quantitative researchers see bias as a source of error, qualitative researchers see their viewpoints as assets in producing meaning.
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For example, when our team interviews students for our studies, we consider how our dual roles as college professors and researchers may influence how we interpret our participants’ experiences, what they feel comfortable sharing and how they share it. Openly sharing such accounting provides important context for readers considering the findings, judging how far they can be applied elsewhere and building trust in the findings.
Anthropic Interviewer is not capable of reflexivity, because it has no frame of reference or capacity for self-reflection. As a machine, it cannot self-monitor its “choices” in interactions, consider how participants perceive it, or reflect on how these factors may shape what participants share or hold back. When readers cannot take stock of the ways researchers’ assumptions, values, beliefs and choices affected how they collected data, this can make the research seem less trustworthy.
Interviewing often helps researchers develop an empathetic connection to their study participants, which can help ensure their work is ethical and accountable. This deeply felt connection can guide researchers in respecting boundaries in interviews.
Empathy also helps researchers take care in honoring the thoughts, feelings and experiences of their participants by representing them as faithfully as possible.
Qualitative interviews still need humans
Anthropic Interviewer introduces new possibilities for qualitative research by enabling data collection at an unprecedented scale and speed. However, this does not mean that it does what human interviewers do in qualitative inquiry.
Research interviewing is not about extracting ready-made insights from research participants as efficiently as possible. It is about entering into other people’s realities and leveraging shared human experiences that make mutual understanding possible, both cognitively and emotionally.
As sociologist Douglas Ezzy once said, good interviews are about communion, not conquest.
Kelley Cotter has received funding from the National Science Foundation.
Priya C. Kumar has received funding from the Institute of Museum and Library Services (IMLS).
Ankolika De does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
