Integrating AI in the Qualitative Research Workflow Appropriately

About

Workshop contents and objectives

This course provides PhD students and researchers with a comprehensive understanding of the contemporary landscape of Artificial Intelligence (AI) in qualitative research. Grounded by ethical considerations and current methodological debates, the course considers the principles and practices of using these technologies throughout the analytic workflow. A range of tools designed to facilitate qualitative research that harness AI in different ways are introduced and participants have the opportunity to experiment with a selection of them, using sample data and their own research materials, as appropriate.

The emphasis of the course is to critically reflect on the potential role and appropriate use of AI-driven tools in qualitative research, and to compare their utility with other tools and human-driven processes. Ethical issues are central, along with how to document the use of tools (whether AI or otherwise) transparently. This includes best practices for integrating AI with human interpretation in qualitative studies, and consideration of when the use of AI is not appropriate. We also discuss the future of qualitative research in the Generative-AI world, reflecting on the impact on methods of these technologies.

Participants will leave the course with a clear understanding of the implications of employing AI in qualitative studies and with practical experience of several tools. The qualitative AI space is evolving quickly, so the tools focused on during this course are subject to change, depending on what is available at the time of the course, but will include tools from across the qualitative-AI space. Students will have free access to all the tools used for the purpose of the course, and will be provided access ahead of the first sessions.

Workshop design

A mix of lectures, demonstrations and discussions, group work, practical exercises with AI tools, individual work with research data, student presentations, and other in-class activities. Students will spend approximately 50% of the course time on practical exercises with research data.

Detailed lecture plan (daily schedule)

Day 1 – Orientation to AI in the Qualitative Research Workflow

>The history of Computer-Assisted Qualitative Data AnalysiS (CAQDAS)
>Genres of computer-assistance in the context of qualitative methodologies
>Types of AI tool for qualitative research: what do they actually do?
>The Generative-AI explosion and its impact on qualitative research practice
>Balancing human interpretation with computer-assistance
>The ethics of using AI for qualitative research
>Independent work: Student planning for own projects
>Student reflections: why consider the use of AI for qualitative research?

Day 2 – AI for Qualitative research Design, Reviewing Literature and Data Collection

>Integrating the use of AI into the qualitative workflow responsibly
>Using Generative-AI for ideation
>Reviewing literature with the assistance of AI
>Independent work: Student work on own research data
>Student reflections: how might the use of AI impact critical reading and thinking?
>AI-assisted data collection – when is this appropriate?
>Student experiences: being on the other side of AI-assisted data collection

Day 3 – Using AI for qualitative data analysis, part 1

>Tools for AI-assisted qualitative analysis
>Generative-AI capabilities in qualitative analysis
>Planning for the use of AI in a qualitative analysis
>AI-driven transcription – what we gain and what we lose
>Data familiarisation with and without the use of AI
>Independent work: Student work on own research data
>Student reflections: role of AI to explore and conceptualise qualitative data

Day 4 – Using AI for qualitative data analysis, part 2

>Qualitative coding with and without the use of AI
>AI-assisted querying to find patterns
>Independent work: Student work on own research data
>Reflection and reflexivity when using AI in qualitative analysis
>Student reflections: is it really my work if I use AI?
>Keeping the human in the loop: AI as assistant not replacement
>Student presentations: what ethical use of AI looks like in my project

Day 5 – Working qualitatively in the world of AI

>Transparency and rigour in the AI-assisted qualitative workflow
>Aligning the use of AI with the values of qualitative research
>Considering the use of AI across the methodological spectrum
>Is AI just another tool?
>Communicating the use of AI in qualitative research projects
>Student presentations: integrating AI into my project – research plans
>Final reflections: where do we go from here?

Course materials

All materials will be provided online.

**The Summer School cannot grant credits. We only deliver a Certificate of Participation, i.e. we certify your attendance.**

If you consider using Summer School workshops to obtain credits (ECTS), you will have to investigate at your home institution (contact the person/institute responsible for your degree) to find out whether they recognise the Summer School, how many credits can be earned from a workshop/course with roughly 35 hours of teaching, no graded work, and no exams.

Make sure to investigate this matter before registering if this is important to you

Course leader

Dr Christina Silver is an Associate Professor (Teaching) and a Director of CAQDAS Networking Project based at the University of Surrey, UK

Target group

graduate students, doctoral researchers, early career researchers

Fee info