The story of 2023 continues to be AI and how it is rapidly evolving and changing work, entertainment, research, science, education, etc. In the qualitative research space, AI is also a hot topic, and at InterQ, we’re excited to be testing out and using tools to help us synthesize research.
AI Use-Cases in Qualitative Research
First, let’s start with the positive: What are the key use cases where we are seeing AI make a big difference in the research field? Below is not a comprehensive list, but the highlights of where we have seen benefits from AI, and how we are using the technology internally.
#1: AI in transcription
In qualitative research, transcription is essential. Because qualitative research involves interview-based research, accurately capturing everything that is said is crucial. For every research interview we conduct, we always transcribe. Before 2023, we had already been using AI tools for transcription. Tools from the past have improved markedly, and now we often use AI transcription to record our qualitative in-depth interviews. We have found that AI still doesn’t work well enough for focus groups, where there are multiple speakers (often at once), and the nuances are very crucial to capture, but for one-on-one in-depth interviews, we are using AI transcription more frequently. The tools we prefer have an ability to easily pause on the transcript and hear the actual audio to help clear up errors (which are still frequent, but improving). The advantages of AI transcription over human transcription are speed of delivery and cost. However, for accuracy, we still prefer human transcription, but we’ll often use AI transcripts as our first pass.
#2: AI in mobile ethnographic research
We’re really pleased to see how far AI is coming along in mobile ethnographic research. In mobile ethnographies, we outfit participants with an app, and throughout the day (or for specific tasks), they respond to questions and record “in the moment” what they’re doing or how they are using digital tools. It’s an excellent way to authentically capture experiences, and it’s one of our favorite research methods.
Our favorite tool, Indeemo, recently came out with AI functionality that sums up responses and themes. We love the time-savings that it builds in, and it allows us to sort through hundreds of responses quickly and see the key themes that emerge. As researchers, this helps us report back to our clients and internal teams so that the research can be synthesized, accurately and quickly.
#3 AI in research synthesis
The final use-case of AI that we would like to highlight is different ways to use various tools in research synthesis. Report writing and going through all of the research data (transcripts, video, audio, etc.) is undoubtedly the most-time consuming aspect of qualitative research. We now rely on AI tools that allow us to import the data, and then tag and sort through key themes, quotes, and ideas. This is not a replacement for human judgment, as there is always way more context that needs to be taken into account (and this is where skilled report writers come in), but it is a great way to help report writers organize and search for keywords and themes.
To summarize: AI is an amazing tool in qualitative research, but by no means a substitute for human judgment
At InterQ, we are really excited to use AI tools as an additional way for us to sort through, organize, and tag data, but it is by no means a substitute for the intricacies and skill-sets that trained researchers have. From working closely with the client, to building the discussion guide, to interviewing, and to contextually writing reports (based on business knowledge, insight-generation, and client/product context), qualitative research requires skills that AI simply can’t replicate, but we do love how the new AI tools help speed up and organize our research process.
