NOTE: Course content is the same as the Integrating AI course offered at QRSI 2026

The capabilities of AI-assisted tools raise significant questions for qualitative research. This course provides a thorough understanding of the landscape of Artificial Intelligence (AI) in qualitative research, covering the principles, practices, and ethics of using these technologies throughout the analytic workflow.

The course introduces a range of tools designed to facilitate qualitative research that harness AI in different ways. Participants will have the opportunity to experiment with a selection of tools, using sample data and their own research materials, if appropriate.

The emphasis of the course is to critically reflect on the potential role and appropriate use of AI-driven tools in qualitative research. This includes the recently emerging Generative-AI tools, and more traditional AI tools that have been available for longer. Ethical issues are central, along with how to document the use of AI transparently, and best practices for integrating AI with human interpretation in qualitative studies. 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. Participants 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. If it is not possible for you to access these tools on your computers, you’ll be able to watch a demonstration on-screen.

This course presents the qualities of a well-planned qualitative inquiry study, introduces principles and philosophies that guide planning and design, and addresses practical and logistical issues related to this evolving process.

Not only is a qualitative study plan detailed and easily understood, its components depend on the project’s evolving nature, underscoring the continual need for flexibility, often addressed using multiple strategies of data generation and analysis. A well-planned study also keeps a realistic timeframe—working “backwards” from the intended goal and deadline—to make the project manageable and responsive to ongoing “on the ground” developments. Through all stages of design, researchers critically use evaluative criteria to assess areas that need particular focus or redirection.

Planning and designing the phases of qualitative inquiry projects include planning for access to research sites and participant recruitment, planning for quality and adequate data generation (sources and approaches), designing appropriate, systematic, and rigorous data analysis (approach, timing, and techniques), and planning for writing and dissemination.

The course will attend to specific principles that guide planning and designing, including:

• foundational assumptions and “logics” underpinning qualitative research and design
• the importance of active planning and responding to a study’s emergent nature
• considering implications of several “full” qualitative research designs (e.g., ethnography, phenomenology, grounded theory)
• attending to the need for alignment between methodological approach and research design and practice
• issues pertaining to a more “general” qualitative approach applicable to all qualitative research designs
• anticipating and planning how to address ethical issues
• examining how to address researcher positionality throughout the research process

The course will include multiple practical exercises to engage participants in developing and refining their knowledge and skills with an emphasis on adequate data generation and analysis and appropriate sequencing of activities.

Course content will draw on the scholarship of Norman Denzin, John Creswell, Kathy Charmaz, Yvonna Lincoln, Egon Guba, Janice Morse, Martyn Hammersley, Kathryn Roulston, and Uwe Flick including the following:

• Charmaz, K. (2014) Constructing grounded theory. 2nd ed. London: Sage
• Creswell, J. W., & Poth, C. N. (2016) (4th ed.). Qualitative inquiry and research design: Choosing among five approaches. London: SAGE
• deMarrais, K., Roulston, K., & Copple, J. (2024) Qualitative Research Design and Methods: An Introduction, Maine: Myers Education Press.
• Denzin, N. and Lincoln, Y. (Eds). (2018) The SAGE Handbook of Qualitative Research, 5th ed., London: Sage
• Flick, U. (Ed.) (2022) The SAGE Handbook of Qualitative Research Design, London: Sage
• Hammersley, M., & Atkinson, P. (2019) Ethnography: Principles in practice, London: Routledge.
• Lincoln, Y., Lynham, S. and Guba, E. (2018) paradigmatic controversies, contradictions, and emerging confluences, revisited, in Denzin, N. and Lincoln, Y. (Eds). The SAGE Handbook of Qualitative Research, 5th ed., London: Sage, pp. 108-150
• Maxwell, J. (2022) Interactive approaches to qualitative research design, in Flick, U. (Ed.) (2022) The SAGE Handbook of Qualitative Research Design, London: Sage, pp. 41.54
• Morse, J. (2022) The fallacy of rigor: Examining checklist criteria as an indicator of quality, in Flick, U. (Ed.) The SAGE handbook of qualitative research design, London: Sage, 373-395
• Roulston, K., & Halpin, S. N. (2022). Designing qualitative research using interview data, in Flick, U. (Ed.) The SAGE handbook of qualitative research design, London: Sage, 667-683.
• Thornberg, R. and Keane, E. (2022) Designing grounded theory studies, in Flick, U. (Ed.) The SAGE handbook of qualitative research design, London: Sage, 452-466
• Vanover, C., Mihas, P. and Saldaña, J. (Eds.), The Analysis and Interpretation of Qualitative Data: After the interview. Thousand Oaks, CA: Sage

Historically, mixed methods studies often involved parallel use of methods, but recently there is more emphasis on integrated mixed methods, which is when qualitative and quantitative components are explicitly related to each other to produce integrated findings. Integrated mixed methods studies are challenging because they involve multiple methodological decision points, interdisciplinary collaboration, and intentionality across all phases of a study.

The purpose of this course is to advance participants’ knowledge of pragmatic strategies and tools that facilitate integration of qualitative and quantitative methods. The first segment of the workshop reviews mixed methods study design options and rationales, multiple decision points in integrated mixed methods research, and conceptualizations of mixed methods specific aims and research questions that will maximize potential for integration and communication of findings.

The second segment of the course focuses on specific components of integrating methods (e.g., in sampling, data collection, data analysis), using models and frameworks (when appropriate) to guide data collection and analysis, exploring analytic options, presenting results (e.g., using joint displays), and reviewing criteria for strong mixed methods publications.

The third segment of the workshop is interactive: participants are encouraged to bring their integrated mixed methods research questions to the session, along with any visuals that they have drafted. We will workshop several examples, providing opportunity for discussion, revision, and innovation.

This course prepares participants to:

1. Characterize different mixed methods study design options and rationales associated with these options
2. Articulate and execute solutions to challenges posed by integrating qualitative and quantitative methods
3. Develop visual representations of integrated mixed methods research components
4. Identify and implement characteristics of good integrated mixed methods products

Reflexive Thematic Analysis (RTA) as described by Braun and Clarke (2021) is an increasingly popular analytic method in qualitative inquiry. This course considers how researchers can use digital tools to enact RTA, including whether the use of Generative AI tools is appropriate for this type of analysis.

We begin with an overview of RTA, locating it within the broader landscape of qualitative data analysis method(ologies), and discussing what it means to be reflexive and to critically engage with and interpret data.

We then map out the landscape of computational support for qualitative analysis, including general-purpose tools (e.g. spreadsheets and word-processing applications), bespoke QDA programs (e.g. ATLAS.ti, Dedoose, MAXQDA, NVivo, Quirkos, etc.), and Generative-AI tools deriving from the capabilities of Large Language Models (LLMs).

The remainder of the course focuses on implementing the phases of RTA using digital tools, including consideration of whether the use of Generative AI can facilitate or hinders reflexive practice and critical engagement with qualitative materials, as has been claimed (Jowsey et al. 2025). To accomplish this, we compare the use of MAXQDA with and without its AI Assist features, with general-purpose Chatbots (e.g. ChatGPT, Claude, MS Copilot, etc.)

The aim of the course is to critically reflect, via practical experimentation, on the role of tools in the RTA process, from ethical and methodological perspectives, in the context of current debates about Generative AI. Participants will leave the course with a clear understanding of the implications of employing Generative AI and other digital tools to enact RTA, and with practical experience.

Participants will have free access to the tools used for the purpose of the course, and will be provided access ahead of the first sessions. If it is not possible for you to access these tools on your computers, you’ll be able to watch a demonstration on-screen.

References:

• Braun V and Clarke V (2021) Thematic Analysis: A Practical Guide. Sage Publications.
• Jowsey, T., Braun, V., Clarke, V., Lupton, D., & Fine, M. (2025). We Reject the Use of Generative Artificial Intelligence for Reflexive Qualitative Research. Qualitative Inquiry, 0(0). https://doi.org/10.1177/10778004251401851