Semi-structured interviews are the most common data generation strategy for many qualitative research approaches. While there is copious advice about the "what" of interview questions (types, phrasing, good practice) and conceptual and theoretical aspects, there is little guidance through the practical process of creating them ("how to"). There is a particular gap for guidance tailored to specific approaches such as interpretative phenomenological analysis (IPA) and reflexive thematic analysis (reflexive TA). This gap is also pronounced relative to the attention devoted to analytic processes and leaves novice researchers inadequately supported in generating appropriate data. This article offers a step-by-step process, particularly for first-time undergraduate or postgraduate researchers, to develop an interview guide from a research question. The guidance focuses on IPA and reflexive TA as popular, commonly taught approaches, to enhance teaching resources and provide practical resources for students. This guidance, developed from the authors' experience as instructors, research supervisors, and researchers, contributes to improving research teaching and practice by providing methodological guidance for research methods courses and independent student research projects. An example is developed throughout as an illustration.
'Open research' (or 'open science') has substantially reshaped conversations surrounding rigor, transparency, and accountability, yet its vocabulary remains largely rooted in quantitative, positivist paradigms. Terms such as reproducibility, replicability, and generalisability are routinely treated as universal markers of research quality, despite their limited epistemological compatibility with many qualitative methodologies. While qualitative scholars have long articulated the limitations this, the rapid emergence of the open research movement creates a new context to synthesise and translate these ideas. Drawing on established traditions and debates, we propose a rearticulation of open research terminology that better reflects interpretive logics and methodological pluralism of many qualitative approaches. Specifically, we advocate for concepts such as 'traceability' to replace 'reproducibility', 'transferability' rather than 'generalisability', and 'dependability' instead of 'replicability'. Such an argument is not new, but rather connects existing qualitative scholarship with emerging conversations about language appropriateness to envision a more inclusive and epistemically just vision of openness.
As generative artificial intelligence (GenAI) becomes increasingly integrated into academia, methodological questions emerge regarding the role of human participants in qualitative knowledge production. This article argues that research tasks requiring participants to generate outputs-that GenAI systems can already simulate-risk reducing meaningful agency and undermining participatory research principles. Drawing on Freire's theory of knowledge co-creation, the article conceptualizes how research can remain ethical and epistemically rigorous amid new technologies. Accordingly, a five-step iterative framework for critical GenAI-informed research methodology is proposed, which shifts participants from mechanical performers toward becoming meaning-makers who critique, reject, and refine AI-generated outputs. Participants thus function as active cocreators of knowledge and critical evaluators of GenAI systems rather than passive data sources, which may mitigate social desirability bias. Additionally, GenAI offers the potential to reduce participants' cognitive burden and improve emotional safety, particularly in research that addresses stigmatized topics intersecting with race, gender, disability, socioeconomic status, or Indigenous ancestry. By foregrounding participants' interpretive authority over GenAI outputs, the proposed framework aims to preserve agency while advancing equity, diversity, and inclusion (EDI) within evolving qualitative research methodologies.
