How AI Transformed Diary Research: Three Compromises We Left Behind

How AI Changed Diary Research: Three Compromises Abandoned

Why the method is so demanding

Diary research produces data you can't get from interviews: real-time behavior, in natural context, without an observer present. A respondent doesn't adapt behavior to the interview situation — they simply live and record. But the price of this naturalness is volume. Dozens of participants, each with a separate chat. Daily entries arrive in different formats: text, photos, voice messages, video clips. Before the analytical phase begins, the team accumulates hundreds of units of unstructured content. Processing them manually means spending many times more resources than on any other qualitative method. This is why three compromises are usually built into the design before fieldwork begins:

• Reduce the sample to a manageable number of participants
• Shorten the diary period to reduce incoming data volume
• Lower the depth of analysis — skim the records rather than analyze them deeply

Each of these undermines the method's value. The team decided not to make these sacrifices and tested what happens if AI is integrated at key stages of the work.

Where AI Takes the Load

AI took on the primary processing of the data stream — the part that consumed most of the analysts' time. Voice messages were transcribed automatically. Photos were described and tagged. Text entries were immediately coded according to a pre-prepared thematic guide — without manual processing of each entry. Parallel processing turned out to be the key advantage: while fieldwork was still ongoing, analysts could already see the first patterns. This changed the team's work rhythm — instead of an analytical sprint at the end, there was steady progress throughout the research.

"We didn't reduce the sample — we shifted routine work to a tool that

handles it faster than we do."

A crucial point: AI did not replace the analyst, but worked as a draft. The researcher verified results, corrected codes, and added interpretation nuances that the tool missed. AI is the first layer, humans are the final layer.

Three Compromises That Disappeared

Sample size. Teams typically take 10–15 participants — more is unmanageable manually. With AI processing, it became possible to work with 30–40 participants without increasing the analyst's workload.

Field duration. The standard approach is two weeks instead of a month. AI allowed preserving the full diary period: processing happens in parallel with collection, not accumulated at the end and placing pressure on the team.

Depth of analysis. When there's a lot of data, the analyst skims and misses details. AI summarization for each participant allows the researcher to focus on patterns and discrepancies rather than spending time decoding raw records.

What This Means

Diary research is no longer a method only for large teams with big budgets. AI removes the operational ceiling and allows obtaining rich qualitative data without three classic sacrifices. But this works only when AI is built into the process as a tool for primary processing — not as a replacement for researcher judgment.