AI in Research: Leveraging AI to Enhance Qualitative Research

Feb 8, 2024

Qualitative research is all about understanding people - their thoughts, feelings, behaviours, and motivations. As qualitative researchers, our role is to uncover deep insights through techniques like in-depth interviews and focus groups. We rely on asking the right questions and interpreting responses to get at underlying truths. And certainly, AI in research has a place, but where is that exact place?

The human touch remains indispensable in qualitative research; AI tools offer new ways to augment our work. Modern AI systems like chatbots can help researchers brainstorm better questions, understand emerging language trends, and tap into volumes of historical data. When used thoughtfully, they have the potential to enrich qualitative insight generation.

The Power of the AI Prompt

AI chatbots like ChatGPT respond best when prompted properly. Asking the right questions sits at the very heart of qualitative research. As our chief insights seeker Isabelle Landreville explains, “It’s about the question, very much like what I do for a living. It’s about the question or the prompt.”  

Rather than asking an overly complex question, Isabelle recommends starting a conversation with an AI assistant by clarifying your mission and mandate. An example prompt could be: “I’m researching perceptions of Brand X among 18-34-year-old females. What questions do you recommend I ask to understand better this target’s relationship with and attitudes towards this brand?”

An AI chatbot can rapidly generate volumes of question ideas based on your prompt. While not every question may be relevant or practical to include in your qualitative approach, having a large pool of ideas to draw from can stimulate your own brainstorming and question optimization. The AI helps remove barriers like staring at a blank page so you can leverage your uniquely human creativity and insight.  

The moderator is still responsible for assessing the qualitative value of different questions. You may want to analyze the AI-generated questions through lenses like:

  • Open-ended vs closed-ended
  • Funnel from broad to specific
  • Projective vs direct
  • Triggers an emotional response
  • Is there a storytelling opportunity

Look at the machine-created questions as a starting point to build from rather than an end solution. Identify and refine the most relevant questions to elicit the best target insights you seek. AI lawnmower questions followed by human gardener fine-tuning.


Read next: Creating the right environment for participants

Understanding Language Trends

Language continuously evolves, especially among younger demographics. Slang terms come into vogue while others fade away. As qualitative researchers, staying on top of the latest lingo used by our target groups is critical for both effectively communicating with them and accurately interpreting what they share.

Interacting regularly with an AI chatbot provides invaluable visibility into shifting language trends. Isabelle explains how she has daily coffee conversations with ChatGPT to “give me a pulse check on the language that folks are using.” For research focusing on teens or youth culture, she may prompt the chatbot by asking for the latest slang used by 13-19-year-olds around topics like dating, fashion, gaming, etc.

The AI chatbot response provides a snapshot of popular terms and phrases in use right now among specific subculture groups. Researchers can then use this language intel to refine questionnaire guides, brief moderators, better converse with participants during interviews/focus groups, and ensure interpretation remains relevant.

Enriching Analysis with Historical Data

AI tools provide instant access to volumes of historical data - far more than any individual’s memory bank. AI connected to historical data can ensure information is easily found and presented within context.

“We have 50 years of experience in doing this. And so, there’s great lessons learned and...great historical data you may not have at your fingertips,” says Isabelle. Their team leverages an internal AI that can rapidly and securely search company servers and surface relevant insights from past research based on prompts.   

Rather than relying purely on personal memory and sifting manually through old reports, AI instantly surfaces relevant gems from the qualitative vaults. This fuels better research design grounded in proven approaches. It also enables historical comparisons to pinpoint cultural shifts and emerging trends over decades. The combined intelligence of both human researchers and AI ultimately leads to higher-quality qualitative endeavors.

The Machine-Human Partnership

While AI systems like ChatGPT contain powerful capabilities, Isabelle emphasizes an effective qualitative approach that combines “the beauty of human creativity and insight” with AI’s speed, language prowess, and recall capacity. Machines and humans play complementary, mutually reinforcing roles.

Chatbots help researchers quickly generate question ideas, understand cultural linguistics, and tap historical data. Human insight then refines and focuses this content on crafting optimized qualitative research plans. People also lead empathetic interviews and interpretations, while machines can analyze volumes of data.

Qualitative research remains a human-centered practice at its core. But thoughtfully integrating new AI tools allows us to better listen, understand, and extract insights from the people we seek to learn from. “It helps us better and more efficiently set ourselves up for success,” concludes Isabelle, while the human art of connection pulls out the deeper meaning.

The future of qualitative inquiry rests on this harmonious melding of human creativity with AI capabilities. AI in research has its place - no doubt. Together, we will uncover richer insights and drive more informed decision-making. Machines can enhance and build upon the context-rich discoveries only we humans can make.