AI Qualitative Coding with Your Own Codebook
Bring the codes and definitions you already use. Evidano applies them consistently across every transcript — with a clickable quote behind each code application, so the coding can be audited like a second coder’s work, not taken on faith.
Why codebook-driven coding is where AI earns its place
Much of the skepticism about AI in qualitative research targets the open-ended case: an unconstrained model inventing themes with no accountable basis. Deductive coding is the opposite situation. The intellectual work — deciding what counts, defining each code, drawing the boundaries — is already done, by you. What remains is applying those fixed definitions to hundreds of files without drift, fatigue, or shortcuts: a consistency task, and precisely the kind of work machines do better at scale than tired humans in week three of coding.
This is also the setting where AI coding can be properly evaluated. With a fixed codebook, agreement between AI and human coders is measurable the same way inter-rater reliability between two humans is measured — which is exactly what published comparisons have done, reporting 96% agreement across 371 transcripts and 92% across 298 reports against expert human analysis.
The codebook workflow, step by step
Prepare your codebook
Assemble your codes with definitions and, ideally, inclusion and exclusion criteria and an example excerpt per code — the same material you would give a new human coder before calibration.
Upload data and codebook
Add your transcripts, documents, or open-ended survey exports — 50+ files per analysis, in 100+ languages — and provide your codebook to Evidano.
Run the deductive coding pass
Evidano applies your codes across the entire dataset consistently — the same definitions applied the same way in transcript 1 and transcript 300, without coder fatigue or drift.
Audit code applications against quotes
Every code application carries a clickable quote back to its exact source passage. Review a sample the way you would run an inter-rater reliability check, and correct any misapplications.
Analyze, compare, and report
Work with frequencies, cross-segment comparisons, and theme summaries built on the coded data, then export evidence-linked findings for your write-up.
No codebook yet? Start inductive, finish deductive
For exploratory studies, Evidano drafts the codebook from your data: an inductive pass proposes codes with definitions and supporting quotes for each. The draft is a starting point for researcher judgment — merge what overlaps, sharpen definitions, discard what is merely frequent — and the refined codebook can then be applied deductively across the full dataset. The hybrid mirrors how many teams already work, with the mechanical passes compressed from weeks to hours.
Either way, the output is built for verification: themes, frequencies, and cross-segment comparisons all trace back to source-linked quotes in your files.
Treat the AI like a coder whose reliability you establish
The defensible workflow is not “trust the output” but “check it the way you would check a new team member’s coding”: independently code a sample, compare, examine disagreements, and document the agreement level in your methods. Because every AI code application links to its evidence, that audit takes hours, not weeks — and reviewers get an inter-rater-style reliability statement instead of a leap of faith. Guidance on disclosure wording and journal AI policies is on how to cite Evidano.
Frequently asked questions
- Can AI apply my existing codebook to new data?
- Yes. Deductive coding is one of Evidano’s core workflows: you provide codes, definitions, and criteria, and the AI applies them across every file in the analysis, attaching the supporting quote for each application so the coding can be audited rather than trusted.
- What makes a codebook work well with AI coding?
- The same things that make it work with human coders: one clear definition per code, inclusion and exclusion criteria, and an example excerpt. Ambiguous, overlapping codes produce disagreement with AI coders exactly as they do between human coders.
- How consistent is AI coding compared with human coders?
- In a peer-reviewed comparison across 371 transcripts, AI-assisted coding with Evidano reached 96% agreement with expert human coders; a UN evaluation synthesis reported 92% across 298 reports. Consistency is also structural: the AI applies the same definition identically in every file, where human coders drift with fatigue and time.
- Can the AI create a codebook for me?
- Yes — run an inductive pass and Evidano proposes a codebook grounded in your data, with definitions and supporting quotes per code. Treat it as a strong first draft: refine it, then re-run deductively with the refined version if you want a fully codebook-driven analysis.
- What is the difference between inductive and deductive coding?
- Deductive coding starts from a predefined codebook — often theory-driven — and applies it to the data; inductive coding builds codes up from the data itself without a preset frame. Evidano supports both, and the common hybrid: an inductive draft refined by the researcher, then applied deductively at scale.
- How should I report AI-assisted coding in my methods section?
- Name the tool, state whether coding was inductive or codebook-driven, describe the human review step (for example, an agreement check on a sample), and follow your journal’s AI policy. The cite-us page provides methods wording used in published studies and citation formats.
Keep exploring
Put your codebook to work
Upload a few transcripts and your codes, run a deductive pass, and audit the results against the linked quotes — free forever, no credit card.