AI is shaking up qualitative research: speeding things up, cleaning messy transcripts, and even identifying hidden patterns.

Sounds amazing, right?

Buuuuuuuut there’s a catch.

When does AI go from helpful assistant to heavy-handed editor, scrubbing out the human insights qualitative research was built on?

In this episode of Everyday AI, we're tackling how to balance AI-powered qualitative research without losing touch with human nuance.

Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Join the discussion: Have a question? Join the convo here.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:

1. Importance of Qualitative Research Training
2. Challenges in AI-Assisted Qualitative Research
3. AI's Role in Data Transcription
4. Human-Led Analysis in Qualitative Research
5. AI's Impact on Data Accessibility
6. Limitations of AI in Qualitative Analysis
7. Future of AI and Qualitative Research
8. Critical Thinking in AI-Assisted Research

Timestamps:
00:00 PhD and Qualitative Research Training

05:07 "Qualitative Research and Multiple Narratives"

08:04 AI in Research: Balancing Automation

09:57 AI-Assisted Data Analysis Caution

16:23 AI's Impact on Qualitative Research

19:19 Analyzing Qualitative Data Interpretation

25:11 Balancing AI in Qualitative Research

Keywords:
Qualitative research, qualitative researcher, AI use in research, AI in qualitative research, AI-informed, human-led, thoughtful AI use, research impacts, health psychology, apprentice plumber analogy, training for PhD students, data analysis, transcribing interviews, transcription time, structured versus unstructured data, meaning making, words as data, interpretive work, subjective interpretation, data set analysis, line by line coding, thematic analysis, story telling in research, ensuring rigor in research, critical thinking in research, efficiency in research processes, literature gap identification, data visualization, AI as an intern, sharing research findings with lay audience, implicit meaning in data, biases in models, accessibility of academic work, academic pressure to publish, research dissemination, insights from research, qualitative research processes, interpretation in qualitative research.

Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Ready for ROI on GenAI? Go to youreverydayai.com/partner