GenAI in Qualitative Data Analysis: Framework-Guided Prompt Engineering in Library Research Practice
Location
School of Law Seminar Room 3.09
Start Date
4-6-2026 11:30 AM
End Date
4-6-2026 12:00 PM
Description
As Generative AI (GenAI) tools become increasingly integrated across the research lifecycle, researchers need practical, reproducible methods for the responsible use of these technologies. This presentation will demonstrate a systematic approach to using GenAI for qualitative data analysis through a case study of thematic coding in a library website usability study at the Singapore Institute of Technology.
Drawing on prompt engineering frameworks like CLEAR, this session will illustrate how structured prompts can maintain academic rigour and enhance the reliability of GenAI-assisted analysis. The presentation will walk through the complete workflow, that is, from initial data preparation and tool selection to iterative prompt refinement and validation of AI-generated themes against manual coding.
Attendees will learn:
• how to apply a structured prompt engineering framework to qualitative data analysis;
• practical strategies for evaluating and selecting appropriate GenAI tools, comparing a few free and paid options;
• techniques to identify and mitigate bias, hallucinations, and contextual misunderstandings in AI output; and
• methods for documenting prompts and processes to ensure reproducibility.
The session will candidly address both successes and limitations encountered during the project, reflecting on the unique position of this librarian researcher to pilot GenAI applications in low-risk research contexts, and providing actionable guidance for exploring AI-assisted qualitative analysis. Attendees will also have the opportunity to participate in hands-on activities throughout the session, collaboratively building and refining a GenAI prompt. Participants will leave with decision-making criteria they can adapt to their own institutional contexts.
Included in
GenAI in Qualitative Data Analysis: Framework-Guided Prompt Engineering in Library Research Practice
School of Law Seminar Room 3.09
As Generative AI (GenAI) tools become increasingly integrated across the research lifecycle, researchers need practical, reproducible methods for the responsible use of these technologies. This presentation will demonstrate a systematic approach to using GenAI for qualitative data analysis through a case study of thematic coding in a library website usability study at the Singapore Institute of Technology.
Drawing on prompt engineering frameworks like CLEAR, this session will illustrate how structured prompts can maintain academic rigour and enhance the reliability of GenAI-assisted analysis. The presentation will walk through the complete workflow, that is, from initial data preparation and tool selection to iterative prompt refinement and validation of AI-generated themes against manual coding.
Attendees will learn:
• how to apply a structured prompt engineering framework to qualitative data analysis;
• practical strategies for evaluating and selecting appropriate GenAI tools, comparing a few free and paid options;
• techniques to identify and mitigate bias, hallucinations, and contextual misunderstandings in AI output; and
• methods for documenting prompts and processes to ensure reproducibility.
The session will candidly address both successes and limitations encountered during the project, reflecting on the unique position of this librarian researcher to pilot GenAI applications in low-risk research contexts, and providing actionable guidance for exploring AI-assisted qualitative analysis. Attendees will also have the opportunity to participate in hands-on activities throughout the session, collaboratively building and refining a GenAI prompt. Participants will leave with decision-making criteria they can adapt to their own institutional contexts.