Introduction

When we ask ChatGPT to summarize a transcript, we get a useful overview of the content — a snapshot of key ideas, but without attention to how those ideas unfold over time. In qualitative data analysis, especially when working with video, time matters. Meaning is shaped not only by what is said, but when and how it is said.

This raises an exciting possibility: what if we could combine the interpretive power of AI with the structural precision of Transana?

Transana already enables researchers to create time-coded transcripts that link text directly to video. If we merge this capability with AI-generated thematic analysis (as explored in the previous blog post), we move beyond static summaries to create interactive, chronological analyses — dynamic transcripts anchored to verifiable video evidence.

Instead of just reading about themes, researchers can now click into the exact moment in the debate, interview, or classroom discussion where those ideas emerge. In this way, AI evolves from a summarizing assistant into a partner for structured, evidence-based analysis.

Why Chronological, Time-Anchored Analysis Matters

In video-based qualitative research, meaning is inseparable from context and timing. A theme is not an abstract idea floating in text — it arises at a specific moment, in response to a question or event, and in relation to what comes before and after.

Chronological analysis preserves this context. By anchoring themes, turns, or strategies to precise time codes, the analysis becomes traceable: we know what was said, when, and under what conditions.

Equally important is the ability to connect insights across multiple time scales:

• Macro level → the overall structure (e.g., economic crisis, foreign policy, closing remarks).
• Meso level → exchanges within those phases (e.g., a back-and-forth about health care).
• Micro level → specific turns, gestures, or rhetorical moves that build meaning second by second.

By weaving these levels together, researchers build analyses that are closer to lived reality — complex, layered, and intelligible. This approach reveals how broad themes emerge from smaller moments, creating a richer picture than summaries alone can provide.

In our example of a presidential debate, this means not only identifying major topics like climate change or health care, but showing how these discussions evolve, shift, and gain meaning over time.

Beyond Themes: Multiple Units of Analysis

In addition, in qualitative research, the unit of analysis depends on the researcher’s question. With AI and Transana working together, researchers can explore and organize multiple types of analytic units, such as:

• Themes → recurring ideas or big knowledge categories.
• Turns of talk → who speaks, when, and how interaction is managed.
• Interactional moves → strategies like agreeing, challenging, or reframing.
• Didactic phases → teaching or learning stages in classroom settings.
• Discursive strategies → rhetorical techniques in political or media discourse.

Each unit provides a different analytical lens — and the combined power of AI and Transana supports all of them.

In this post, we’ll focus on thematic analysis using a U.S. presidential debate as an example, showing how AI results can be transformed into interactive, time-coded transcripts that preserve both meaning and chronology.

From AI Notes to Time-Coded Transcripts: The Workflow

1. Generate AI Results

Start with a transcript and run a thematic analysis prompt (as described in the previous post). The AI produces a note with themes, each marked by start and end times.

Example of a prompt used to generate an interactive transcript:

You are a researcher in presidential debates
Your task is to analyze the following transcript.
Identify big knowledge ideas (themes) that are jointly built through iterative interactions between the candidates and the audience.
Work chronologically through the transcript.
For each theme, detect its start time (when the idea begins), give a short, meaningful title (2–6 words), and its end time (when the discussion shifts to a different theme).
Use verbal transitions (e.g., a new question, a shift in concept, reframing, or proposing alternative explanations) to decide boundaries.
Only mark the big knowledge-building themes, not small digressions or procedural talk.
Format your output exactly as follows (no extra text):
HH:MM:SS.sss Beginning —
HH:MM:SS.sss End

2. Clean the Note

Before converting, clean the text:

• • Ensure the structure follows this format:
    [HH:MM:SS.sss] <Unit of analysis> [HH:MM:SS.sss]
    In our case we will use the following format
    [HH:MM:SS.sss] <Theme> [HH:MM:SS.sss]
  • Remove formatting errors and duplicate time codes.
  • Simplify or rename theme titles if needed.

3. Create a New Transcript

Copy the cleaned note into a new transcript in Transana. This transforms the AI output from a static note into a transcript that can link directly to the video.

4. Apply Text Time-Code Conversion

In the Transcript window, go to Document → Text Time Code Conversion. Transana automatically synchronizes the text with the media file using the time codes provided.

5. Explore the Thematic Transcript

You now have an interactive transcript where each theme is linked to its corresponding video segment. Clicking on a theme jumps directly to the relevant moment in the debate.

Once created, this transcript becomes more than a display, it becomes a foundation for deeper analysis. Researchers can now utilize it into Transana’s collections, clips, coding layers, and visual representations. The AI-generated, time-coded transcript is not just an end product; it’s a powerful way into the full interactive-analytic use of Transana.

Author Profile

Zeynab Badreddine, Ph.D. is the Founder & CEO of Advanced Video-Based Research LLC, a consultancy dedicated to qualitative video data analysis. She is an official Transana trainer, and a specialist in helping researchers use video, transcripts, and digital tools to produce rigorous and innovative analysis. She works closely with the Transana developer to expand the tool’s global reach.

With a background in science education, management, and computer science, Zeynab bridges research and practice by combining deep academic expertise with hands-on experience in technology-enhanced analysis. Her work focuses on making complex video-based research methods accessible, transparent, and impactful for scholars and practitioners around the world.

Connect with Zeynab on LinkedIn or explore her work at Advanced Video Based Research.