How PhD Students Can Code Interview Transcripts Using AI Generated Notes
Check suggestions against the original interviews and keep the final analytical decisions in your hands. The workflow ahead starts by clarifying what AI can contribute and what remains your responsibility as the researcher.
How PhD Students Can Code Interview Transcripts Using AI Generated Notes
You have several recorded interviews, a research question to answer and no clear place to begin. For PhD students, that first stretch of qualitative analysis can feel like a lot of material to hold in mind at once. [Explore INKKD Features (https://www.inkkdai.com/#features)
INKKD can turn recorded research interviews into searchable text, giving you a practical starting point for reviewing what participants said. You can use AI Chat to explore possible codes, then keep generated summaries and observations in AI Notes as you work.
A code is a short, consistent label that captures an idea, behaviour, experience or perspective in a passage of qualitative data. AI-generated notes can help you find your way through transcripts, but they do not decide what your findings mean.
Upload Interviews and Create Searchable Transcripts in INKKD
A student might bring one recorded research interview into INKKD, then work from its searchable transcript. Upload the intended recording and confirm the resulting text belongs to the correct interview before treating it as analysis material.
Searchable text makes it easier to read and revisit conversations during qualitative data analysis. It gives you a working record for reviewing what a participant said and locating passages relevant to your research. The transcript is not reliable simply because it has been created from audio.
| Material | What It Is Used For | Check Before Moving On |
|---|---|---|
| Interview recording | Source conversation | Confirm it is the intended recording |
| Searchable transcript | Text for reading and analysis | Check availability and correct interview |
| Working transcript | Text ready for coding | Correct participant names and technical terms |
Add the Research Recordings
Bring the recorded research interviews you are permitted to process into INKKD. Keep each recording tied to the right interview in your own workflow. That link matters when you later need to check a passage against the original conversation.
The table describes materials and checks in your research process. It does not describe separate INKKD product features. Start with the source recording, confirm the searchable text is available, then make a dependable working transcript.
Work From Searchable Text, Not Unchecked Audio Conversion
A searchable transcript supports reading, analysis and later review. It also lets you find words or ideas without replaying an entire conversation each time. Still, a transcript is only a starting text until you have checked it carefully.
If a transcript is incomplete, hard to read or missing, resolve the problem before coding. A gap in a key answer can change what you think a participant experienced. Do not treat a failed conversion as a usable account just because some text appears.
Once you have a transcript that corresponds to the correct interview, read and correct it before asking AI to suggest codes. The next step is careful review, not a rush to generate labels.
Explore Possible Codes With INKKD AI Chat
You have read a transcript and want questions to guide a first conversation with AI Chat. Ask for possible directions, then judge every suggestion against your research questions and the interview itself.
AI Chat can help you explore codes connected to your PhD research questions, theoretical framework or recurring participant experiences. It does not independently know the full context of your study. A useful response is still a candidate for your review, not an approved code.
Ask Open Questions Before Suggesting Labels
Begin with a broad prompt if you are still learning what an interview contains. For example, ask, “What topics or experiences stand out in this transcript?” The answer can give you ideas to examine, but it should not replace your own first reading.
A specific mistake is prompting for themes before becoming familiar with the interviews. A polished answer may make an idea feel established before you have checked whether participants raised it. Read first, then use an open question to support exploration.
Connect Prompts to the Study’s Questions and Framework
When your focus is clear, name the research question or framework in the prompt. For example, ask, “What possible codes relate to my research question about access to support?” That gives AI Chat a more focused task without making its response authoritative.
You can also ask, “What participant experiences might complicate my initial interpretation?” This can help you look for a tension or exception you might otherwise miss. Check any suggested complication against the actual passage and its context.
