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The State of AI in Qualitative and Mixed Methods Research

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AI adoption has outrun confidence

Researchers have already made AI part of the qualitative research workflow. What they haven't done is decide it can be trusted with the work that matters most. This report lays out exactly where that line sits, grounded in a survey of qualitative and mixed methods researchers across academic and commercial settings.

57%

of researchers already use AI regularly or occasionally

87%

are concerned about accuracy and hallucination risk

57%

cite distrust of AI outputs as their top barrier

61%

manually review AI's output against source data themselves

48%

say AI should only generate suggestions for a human to review—never decide

What the data shows

Adoption concentrates in specific groups

Regular use rises with experience and varies sharply by role and sector—it isn't the newest researchers driving adoption.

Trust tracks with verifiability

Researchers trust AI most where its output is easy to check and least wherever a task demands real interpretation.

Researchers are filling the gap themselves

Manual verification and independent cross-checking are standing in for rigor most AI tools don't provide.

See what's inside

What researchers are saying about AI

“AI helps me a lot to do brainstorming, discussion and providing consideration. I also use AI to help me to define my thoughts clearer in English, since my first language is not English. However, the thing that I need to be cautious [about] is the AI hallucinations in providing the information.”

Academic researcher – faculty, postdoc, or equivalent
“AI output treated as a first draft or second opinion, never a final answer, especially for interpretation, coding qualitative data, or synthesizing literature. A clear rule: AI can suggest, a human must confirm.”

Independent / consultant researcher
“Sometimes I read material that is too dense for me to understand. I ask AI to paraphrase into words that are simple.”

Doctoral researcher / PhD student
“I feel like privacy is key. It would be helpful if universities, institutions, etc., had an approved list of AI tools and how they may be used.”

Doctoral researcher / PhD student
“Disclosure is key, and so is the ethical use of AI without infringing on the rights of others.”

Research student—undergraduate or master’s

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See where AI earns trust

Discover key findings on how researchers are adopting, trusting, and governing AI in their qualitative and mixed methods work.

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