EP 525: AI-Informed, Human-Led: Thoughtful AI Use in Qualitative Research

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 newsletterMore on this Episode: Episode PageJoin the discussion: Have a question? Join the convo here.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [email protected] with Jordan on LinkedInTopics Covered in This Episode:Importance of Qualitative Research TrainingChallenges in AI-Assisted Qualitative ResearchAI's Role in Data TranscriptionHuman-Led Analysis in Qualitative ResearchAI's Impact on Data AccessibilityLimitations of AI in Qualitative AnalysisFuture of AI and Qualitative ResearchCritical Thinking in AI-Assisted ResearchTimestamps:00:00 PhD and Qualitative Research Training05:07 "Qualitative Research and Multiple Narratives"08:04 AI in Research: Balancing Automation09:57 AI-Assisted Data Analysis Caution16:23 AI's Impact on Qualitative Research19:19 Analyzing Qualitative Data Interpretation25:11 Balancing AI in Qualitative ResearchKeywords: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) Head to AI.studio/build to create your first app.  Head to AI.studio/build to create your first app.  Head to AI.studio/build to create your first app. 

There are no comments yet.

Please log in to write the first comment.