qualitative-research
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- PaperEdArXiv (OSF Preprints)17 Jul 2026
Designing and Evaluating an Integrated AI-Based Educational System for Enhancing Critical Analysis Skills in Pre-Service Teachers
Hossein Talebzadeh
An integrated AI-based educational system was designed and evaluated to enhance pre-service teachers' critical analysis skills. A qualitative case study with history and social science teachers revealed dual-layered technical and human challenges, but also showed that AI can act as a 'pedagogical assistant' by engineering cognitive conflict to foster transformative learning. The effectiveness of AI depends on its integration into a structured educational system that reframes challenges as learning opportunities.
Original abstract
Given the emerging challenges and opportunities of generative artificial intelligence (GenAI) in teacher education, this study designs, implements, and evaluates an integrated educational system aimed at enhancing the critical analysis skills of pre-service teachers. This qualitative case study was conducted with the participation of history and social science pre-service teachers at Farhangian University. Data documenting their experiences within an AI-assisted content analysis project was collected via team and individual evaluation forms and analyzed using the thematic analysis method. The findings revealed dual-layered technical and human challenges, alongside multifaceted technical, pedagogical, and soft skill learning outcomes. More importantly, the results demonstrated a dialectical relationship between challenge and learning, conceptualizing the role of AI as a "pedagogical assistant" that fosters transformative learning by deliberately engineering cognitive conflict. We conclude that the effectiveness of AI in teacher education depends on its integration into a meticulously structured educational system—one where challenges are systematically reframed as learning opportunities and technology serves as a catalyst for shaping the professional identity of innovative educators.
- PaperApplied Linguistics16 Jul 2026
AI and the simplification of task design
Anna Mendoza
This commentary questions whether Generative AI should be used in academic writing instruction and qualitative research, arguing that AI simplifies task representations that human experts have made more complex. Drawing on experiences as an EAP instructor and qualitative researcher, the author contends that AI hinders learning by standardizing language and missing contextual nuances. The paper concludes that both writing students and novice researchers need to engage with un-simplified tasks rather than rely on AI.
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 illustrates integration in qualitatively oriented mixed methods data analysis through a worked example of a TESOL graduate's professional experiences. The authors combined quantitative indicators and qualitative structural analysis of visual network data with reflexive thematic analysis and qualitative comparative analysis of textual interview data. The integration of these analyses provided a holistic understanding of how network features and practices influenced the graduate's professional experiences.
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.