Workshop: Advancing Qualitative Methods with AI: Potential, Challenges, and Ethical Implications

Goals

This workshop offers theoretical considerations as well as the opportunity to gain practical experience with the latest technologies and methods in qualitative data analysis.

Content

In this two-day workshop, you will explore the current landscape of AI in qualitative research. We begin with an introduction to Large Language Models (LLMs), focusing on their underlying mechanisms. This foundation will help us understand and critically evaluate current discussions on the use of generative AI. Next, we delve into the practical application of AI in existing Qualitative Data Analysis Software (QDAS), specifically examining MAXQDA and QualCoder AI; two systems that (still) require researchers to code data.

We then address three crucial topics: bias, data protection, and ethics. These considerations are essential for the responsible use of AI in qualitative research. This raises the question of whether generative AI could not be used more effectively by rethinking the methodological process. Promising approaches are Query-Based Analysis (QBA) by Morgan (2024) and ‘Conversational Analysis with AI’ developed by Friese (2024), in which researchers interact directly with AI systems. As prompt writing is a key component of this approach, you will learn to design effective prompts. This skill is valuable throughout various stages of qualitative research. With this competence, we will experiment with and test the AI-powered tool for qualitative data analysis QInsights. We will wrap up the workshop with a discussion on the implications of using AI tools throughout the research process and how to effectively report these methodologies in academic writing.

Methods

A mix of presentations, discussions, and hands-on exercises on the computer.
Please bring your own device to the workshop, ideally fully charged as we have only limited charging options. Thank you.

In cooperation with

Competence Center for Empirical Research Methods

• Introduction to Large Language Models
• Generative AI in traditional QDAS based on coding
• Bias, data protection and research ethics related to the use of generative AI
• Prompting
• New approaches of AI-supported qualitative data analysis: QBA and Conversational Analysis with AI
• The use of AI tools in academic writing