qualitative-research
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- PaperApplied Linguistics16 Jul 2026
AI and the simplification of task design
Anna Mendoza
This commentary argues that Generative AI simplifies task design in academic writing and qualitative research, potentially hindering deeper learning. The author contends that AI's standardized representations of writing tasks undermine the nuanced understanding needed in EAP instruction, and its limited role in qualitative data analysis overlooks content beyond text. The piece concludes that over-reliance on AI may detract from what students and novice researchers need to learn.
Original abstract
This commentary begins by summarizing Jeon et al.’s (2025) concerns about Generative Artificial Intelligence (GenAI)’s tendency to standardize language, followed by Hu’s (2026) response, on how to bring Artificial Intelligence (AI) into teaching (academic) writing in ways that allow for negotiation of forms. My own response, given the bigger picture, is whether AI needs to be used at all. First, as a former English for Academic Purposes (EAP) writing instructor, I argue that AI represents writing tasks differently from humans, and that going along with its simplified representation is a step back after human experts have rendered more complex representations of the tasks. Second, as a qualitative researcher, I argue that AI is of limited use in qualitative data analysis, since what qualitative researchers analyze goes beyond what is in the text. I conclude that in both these cases, AI simplifies tasks and detracts from what students of academic writing or novice qualitative researchers need to learn.
- PaperTESOL Quarterly9 Jul 2026
Integration in Qualitatively Oriented Mixed Methods Data Analysis: A Worked Example of a TESOL Graduate's Professional Experiences
Yi Sun, Xuesong (Andy) Gao
This paper demonstrates integration strategies in qualitatively oriented mixed methods data analysis using a worked example of a TESOL graduate's professional experiences. It combines quantitative indicators and qualitative structural analysis of visual network data with reflexive thematic analysis and qualitative comparative analysis of textual interview data to develop holistic inferences. The study makes explicit the complex knowledge construction process in TESOL through layered analytical integration.
Original abstract
This paper focuses on integration in the data analysis of a qualitatively oriented mixed methods study, illustrating this process through a worked example of interactions with social actors shaping a TESOL graduate's professional experiences. The paper outlines our analysis of visual and textual data from interviews that employed an embedded social network data elicitation tool. We developed and integrated layered descriptions and interpretations using the following strategies: (1) quantitative indicators and qualitative structural analysis to detail network features of visual data and facilitate further analysis and (2) reflexive thematic analysis procedures and qualitative comparative analysis strategies to explore patterns within the textual data. These analyses were brought together to develop a holistic understanding of network features and associated practices, as well as their influence on the TESOL graduate's professional experiences. Finally, this paper highlights how the application of multiple data analysis strategies can support the development and integration of layered inferences, thereby making explicit the complex process of knowledge construction in TESOL within a qualitatively oriented mixed methods approach.