The Living Archive: Reimagining Qualitative Data Analysis
Melika Ziba Divya Darshni SureshShabnam Hayatgheibi
“Sometimes the most powerful human stories in research get buried on page 37 of a transcript.”
Social Science Instructor
Literature Review
Resources
About The Qualitative Data Analysis Challenges
Qualitative research often involves large, diverse, and complex data that can be difficult to organize and analyze (Malterud, 2001).
Strategies such as triangulation, reflexivity, member checking, peer debriefing, and data management software can strengthen credibility and trustworthiness (Dahal, 2025).
Qualitative data analysis is a cyclical process that should be iterative in order to produce meaningful findings (Suter, 2012).
Key challenges include researcher bias, unclear saturation, time-intensive analysis, and maintaining methodological rigor (Dahal, 2025).
Clear documentation and transparent reporting of coding decisions are essential for preserving the quality and trustworthiness of qualitative research (Dahal et al., 2024).
Overreliance on GenAI may weaken researchers’ qualitative thinking, interpretation, and creativity (Chiu, 2023).
Who really needs help with this problem?
- Researchers drowning in transcript-heavy data
- Researchers with big teams with members from different countries
- Graduate students with limited time and limited support
- Audiences underserved by text-only research communication
- Teams trying to preserve the human story in qualitative data
Problem
Qualitative researchers, particularly graduate students, face an information overload where rich interview data is "buried" in dense, 50-page transcripts that stakeholders often skim or ignore. This creates a knowledge bottleneck and a massive accessibility gap for different learning styles. Traditional text-based reports strip away the emotional context and "human" element of the data, making it difficult for visual or auditory learners to engage with the findings.
“What if qualitative data could be heard, and seen, not just read?!”
Solution
The "Living Archive" is an AI-powered platform that transforms raw interview data into a multimodal experience. It will turn key themes into narrated video segments and emotional soundscapes!
+ The Vision
Example video!
This video was created with Sora AI, Luma AI, and ChatGPT step-by-step. I edited the video using Canva and applied transitions there.
🎥
Enhanced video!
This video was created with Eleven Labs step-by-step. I edited the video in Canva and applied transitions.
🎥
Prompting
For generating each video separately
"Create a realistic, interview-style video with five different participants answering the same set of questions. Each participant should appear in a different university-related setting (for example: a lecture hall, a faculty office, a campus courtyard, a library, and a seminar room). Ensure the lighting and atmosphere feel natural and academic. Smoothly fade in to Participant 1 (an Asian woman, Changing for each video), who answers the following question in their university setting. I also want the voices to sound like real humans, very realistic, showing the emotions, ups and downs, frustrations, and excitement! How prepared did you feel for teaching at the beginning? "I had taught before as a graduate assistant, so I wasn’t completely new to the classroom.” (Changing for each video) At the end, fade out to black can be displayed on screen while soft, thoughtful background music plays.
🤩
Process
Vibe Coding and Dashboard Development
For generating the dashboard
- Prototyped the dashboard in Base44 using AI-generated code and interface design
- Defined the platform through detailed prompts describing the research problem, user needs, and feature flow
- Populated the system with sample interview data to test transcript, summary, infographic, and video workflows
- Simulated major AI functions such as transcription, anonymization, clip extraction, and thematic organization
- Refined the prototype through multiple iterations to improve usability, storytelling, and presentation quality
Click to visit
Click to visit
Social Impact
- Expands access to qualitative research by making dense findings easier to understand and engage with
- Turns long transcripts into formats that are easier to navigate, faster to catch up on, and easier to digest
- Helps collaborators and stakeholders get on the same page more quickly
- Makes research findings easier to share across teams, classrooms, and communities
- Improves recall and long-term memory by presenting findings in more engaging multimodal forms
- Preserves the human voice, emotion, and context behind the data instead of losing them in static text
- Supports broader audiences with different preferences for how they process information
Limitations & Future Directions
Limitations & Future Directions
Beyond research: The Living Archive can support text-heavy fields such as medicine, law, and literature
- Medicine: Can cluster similar patient cases and specialist transcripts to support teaching, review, and pattern recognition
- Literature & culture: Can give autobiographies, oral histories, and folklores a stronger voice through multimodal storytelling
- Core promise: Restores the human voice and emotional depth often lost in static PDFs
Challenges / future considerations:
- AI hallucinations and imperfect interpretations require a human-in-the-loop model
- Cost and latency may affect scalability and real-time generation
- Ethical considerations include privacy, consent, representation, and responsible editing
- Trust and accuracy must remain central in any high-stakes use case
Do you think the "Living Archive" can help you as well?!
Thank You!
The Vision
We aren't just making a video; we're creating a menu of outputs. At the end of each thematic journey, the user can choose how they want to digest the data: an AI-generated infographic for a quick summary, a detailed storyboard to visualize the narrative, a synthesized podcast for deep listening, or an interactive story to navigate the data themselves.
The Living Archive: Reimagining Qualitative Data Analysis
Melica Ziba
Created on April 15, 2026
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Transcript
The Living Archive: Reimagining Qualitative Data Analysis
Melika Ziba Divya Darshni SureshShabnam Hayatgheibi
“Sometimes the most powerful human stories in research get buried on page 37 of a transcript.”
Social Science Instructor
Literature Review
Resources
About The Qualitative Data Analysis Challenges
Qualitative research often involves large, diverse, and complex data that can be difficult to organize and analyze (Malterud, 2001).
Strategies such as triangulation, reflexivity, member checking, peer debriefing, and data management software can strengthen credibility and trustworthiness (Dahal, 2025).
Qualitative data analysis is a cyclical process that should be iterative in order to produce meaningful findings (Suter, 2012).
Key challenges include researcher bias, unclear saturation, time-intensive analysis, and maintaining methodological rigor (Dahal, 2025).
Clear documentation and transparent reporting of coding decisions are essential for preserving the quality and trustworthiness of qualitative research (Dahal et al., 2024).
Overreliance on GenAI may weaken researchers’ qualitative thinking, interpretation, and creativity (Chiu, 2023).
Who really needs help with this problem?
Problem
Qualitative researchers, particularly graduate students, face an information overload where rich interview data is "buried" in dense, 50-page transcripts that stakeholders often skim or ignore. This creates a knowledge bottleneck and a massive accessibility gap for different learning styles. Traditional text-based reports strip away the emotional context and "human" element of the data, making it difficult for visual or auditory learners to engage with the findings.
“What if qualitative data could be heard, and seen, not just read?!”
Solution
The "Living Archive" is an AI-powered platform that transforms raw interview data into a multimodal experience. It will turn key themes into narrated video segments and emotional soundscapes!
+ The Vision
Example video!
This video was created with Sora AI, Luma AI, and ChatGPT step-by-step. I edited the video using Canva and applied transitions there.
🎥
Enhanced video!
This video was created with Eleven Labs step-by-step. I edited the video in Canva and applied transitions.
🎥
Prompting
For generating each video separately
"Create a realistic, interview-style video with five different participants answering the same set of questions. Each participant should appear in a different university-related setting (for example: a lecture hall, a faculty office, a campus courtyard, a library, and a seminar room). Ensure the lighting and atmosphere feel natural and academic. Smoothly fade in to Participant 1 (an Asian woman, Changing for each video), who answers the following question in their university setting. I also want the voices to sound like real humans, very realistic, showing the emotions, ups and downs, frustrations, and excitement! How prepared did you feel for teaching at the beginning? "I had taught before as a graduate assistant, so I wasn’t completely new to the classroom.” (Changing for each video) At the end, fade out to black can be displayed on screen while soft, thoughtful background music plays.
🤩
Process
Vibe Coding and Dashboard Development
For generating the dashboard
Click to visit
Click to visit
Social Impact
Limitations & Future Directions
Limitations & Future Directions
Beyond research: The Living Archive can support text-heavy fields such as medicine, law, and literature
- Medicine: Can cluster similar patient cases and specialist transcripts to support teaching, review, and pattern recognition
- Literature & culture: Can give autobiographies, oral histories, and folklores a stronger voice through multimodal storytelling
- Core promise: Restores the human voice and emotional depth often lost in static PDFs
Challenges / future considerations:Do you think the "Living Archive" can help you as well?!
Thank You!
The Vision
We aren't just making a video; we're creating a menu of outputs. At the end of each thematic journey, the user can choose how they want to digest the data: an AI-generated infographic for a quick summary, a detailed storyboard to visualize the narrative, a synthesized podcast for deep listening, or an interactive story to navigate the data themselves.