How AI Changed Diary Studies: Three Trade-Offs the Team Dropped

A UX research team has shared its experience of bringing AI into diary studies, and explained why it was able to drop three standard compromises that are usually baked in at the design stage.

Why the method is so heavy

A diary study yields data you cannot get from interviews: behavior in real time, in its natural context, with no observer present. Respondents do not adapt their behavior to a survey setting; they simply live and record.

But the price of that naturalism is volume. There are dozens of participants, each with a separate chat. Daily entries arrive in different formats: text, photos, voice messages, video circles. Before the analysis phase begins, the team accumulates hundreds of pieces of unstructured content. Processing them by hand takes many times more resources than any other qualitative method.

That is why three compromises are usually built into the design before fieldwork even starts:

• Cut the sample to a manageable number of participants
• Shorten the diary period to reduce the volume of incoming data
• Reduce the depth of analysis: skim the entries instead of reading them closely

Each of them erodes the value of the method. The team decided not to make these sacrifices and tested what happens when AI is connected at key stages of the work.

Where AI takes the load

AI took over the first-pass processing of the data stream, which was exactly what consumed most of the analysts' time. Voice messages were transcribed automatically. Photos were described and tagged. Text entries were coded right away against a pre-built thematic guide, with no manual review of each entry.

Parallel processing turned out to be the key advantage: while fieldwork was still running, analysts were already seeing the first patterns. This changed the team's rhythm: instead of an analysis sprint at the end, the work moved evenly throughout the study.

"We did not shrink the sample.

We handed the routine to a tool that handles it faster than we do."

The crucial point: AI did not replace the analyst but worked as a draft. The researcher verified the results, adjusted the codes and added interpretive nuances that the tool missed. AI is the first layer; the human is the final one.

The three compromises that disappeared

Sample. Teams usually take 10–15 participants, since more cannot be processed by hand. With AI processing, it became possible to work with 30–40 participants without increasing the analyst's workload.

Field duration. The standard move is two weeks instead of a month. AI made it possible to keep the full diary period: processing runs in parallel with collection rather than piling up at the end and pressuring the team.

Depth of analysis. When there is a lot of data, the analyst skims and loses details. Per-participant AI summarization lets the researcher focus on patterns and divergences instead of spending time deciphering raw entries.

What this means

The diary study is no longer a method reserved for large teams with big budgets. AI lifts the operational ceiling and makes it possible to collect rich qualitative data without the three classic sacrifices. But this works only when AI is built into the process as a tool for first-pass processing, not as a replacement for the researcher's judgment.