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Exploring an Educator’s Experience in Higher Education During Generative AI Transformation

Aug 2026 · IAFOR Journal of Education · 0 citations

TL;DR

This study explored how a higher education educator experienced and made sense of generative artificial intelligence within teaching, peer collaboration, and assessment practices, and identified three superordinate themes, including divergent peer adoption of GenAI, pedagogical adaptation and uncertainty, and assessment ambiguity.

Abstract

This study explored how a higher education educator experienced and made sense of generative artificial intelligence (GenAI) within teaching, peer collaboration, and assessment practices. Using an Interpretative Phenomenological Analysis (IPA) research design, the study focused on a single participant, who is a curriculum leader and instructor at a private European university undergoing rapid shifts in GenAI policy and practice. Data were collected through a semi-structured interview and participant pre-account and analyzed using IPA’s idiographic and interpretative procedures. Findings identified three superordinate themes, including divergent peer adoption of GenAI, pedagogical adaptation and uncertainty, and assessment ambiguity. These illustrate GenAI as an ongoing disruption that reshapes collegial dynamics, teaching approaches, and conceptions of learning and authorship. While GenAI supports accessibility and content simplification, it also raises concerns about student dependency and academic integrity, prompting a shift toward evaluating students’ critical engagement with AI outputs. Interpreted through Mezirow’s transformative learning theory, GenAI emerges as a sustained disorienting dilemma, producing gradual, relational, and ongoing perspective transformation in higher education practice. Future research should extend this work beyond a single-case design to include comparative and longitudinal studies across disciplines and institutions, further examine GenAI-informed assessment models, and the extent to which existing theoretical frameworks capture the relational and evolving nature of AI-mediated educational change.

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