Introduction: Why AI is stepping into the researcher’s toolkit
At its core, user research is a deliberate choice. It’s the act of pausing assumptions and asking: What do our users want? Where are they struggling? What motivates them? What needs have we missed?
Researchers have generated a diverse collection of approaches, to address these issues over the last several years. Some studies examine the reasons for users’ feelings providing researchers with in-depth qualitative data. Others zoom out to measure how many or how often, producing quantitative evidence. Some are attitudinal – focused on what people say they believe. Others are behavioural – centred on what people actually do. The timing can shift too: formative research is about shaping early ideas, while summative research evaluates a nearly finished design.
This isn’t just academic categorisation.It represents the reality that research is dynamic, situated, and multifaceted. And this is what makes AI exciting: not because it ultimately replaces the existing methods, but because of the different ways in which it fits into these methods, speeding up some processes, transforming others, and providing new opportunities altogether.
The many lenses of research (and how AI fits in)
When you break research down, you see a set of contrasting lenses:
Qualitative vs Quantitative
- Qualitative research searches for meaning. It is subjective, exploratory, and messy, typically conducted through interviews or other observational methods and open-ended questions.
- Quantitative research searches for measurement. It is objective, structured, and scalable, typically through surveys, analytics, and statistical testing.
- Where AI fits: Large language models may be used as lightweight interviewers or co-pilots in qualitative inquiries. Machine learning models may be used to analyze large datasets for quantitative patterns.
Attitudinal vs Behavioral
- Attitudinal research is about what users say.
- Behavioural research is about what users do.
- Where AI fits: Sentiment analysis of user feedback, survey responses, and support tickets gives scalable attitudinal insight. On the behavioural side, AI can model and predict click paths, heatmaps, or even simulate drop-off points before a feature launches.
Formative vs Summative
- Formative studies guide early design decisions.
- Summative studies validate whether the final solution works.
- Where AI fits: Generative AI tools can brainstorm and stress-test concepts in formative stages. Automated usability platforms can scale summative testing, running thousands of task completions in minutes.
Generative vs Evaluative
- Generative research expands possibilities – “What could we build?”
- Evaluative research narrows them – “Did this work?”
- Where AI fits: Synthetic participants and AI-simulated personas give early directional signals in generative work. In evaluative phases, AI-driven A/B testing or multivariate simulations help teams converge on better choices quickly.
Contextual vs Controlled
- Contextual studies happen in natural environments – homes, workplaces, on the go.
- Controlled studies happen in labs or structured digital environments.
- Where AI fits: AI video-tagging or wearable integrations allow ethnographic studies without invasive manual observation. In controlled tests, AI moderation and adaptive questioning keep sessions focused.
Longitudinal vs Cross-sectional
- Longitudinal research tracks user experiences over time.
- Cross-sectional research captures a single snapshot.
- Where AI fits: AI diary apps can prompt users over weeks or months, auto-summarising entries. Cross-sectional studies benefit from survey clustering that can reveal hidden segments in minutes.
Primary, Secondary, Hybrid
- Primary = direct user contact. Secondary = analysing existing sources. Hybrid = blending both.
- Where AI fits: Conversational AI can conduct lightweight interviews (primary); scraping AI can scrape forums and app reviews (secondary); and hybrid models can combine both in real time.
These categories are not comprehensive and they are not mutually exclusive. But thinking through them helps us see the different doors through which AI can enter research.
AI will serve as a powerful tool for researchers — it will complement human insight rather than replace it. AI can streamline workflows, connect patterns, and analyze in a manner that supports a deeper understanding of various research methods. When researchers understand how to leverage AI, teams can blend efficiency with descriptive insight. Part 2 will focus on how AI is transforming qualitative research through many methods, with some applicable tools and real examples of integrating AI to illuminate user insights.
