Illuminating complex student realities of artificial intelligence through an entangled pedagogy framework

Abstract The emergence of widely available Generative AI (GenAI) in higher education demands nuanced responses that acknowledge the diversity of student contexts, conditions, practices and perspectives. Developing such nuanced responses requires attention to how students themselves experience and negotiate GenAI within the complexity of their everyday study and lives. In this paper, we focus on three participants from a broader set of focus groups to explore their perspectives on GenAI in relation to their studies, identities, and practices, and how contextual and personal factors shape these views. Using Fawns’ (2022) entangled pedagogy framework to guide analysis, we illuminate some complex entanglements that characterise students’ engagement with GenAI. Our analysis reveals how our students’ views were entangled with conceptions of learning, institutional governance, moral positions, and contextual considerations. By focusing on just three participants, we were able to examine the tensions and contradictions in how their relationships with GenAI were shaped in relation to circumstances, purposes, values, practices, identities, institutions, educators, peers, and perceived future trajectories. Our results suggest that meaningful action requires creating collaborative spaces where students’ voices are heard, fostering approaches that acknowledge the sociomaterial nature of learning with emerging technologies. We therefore argue that appropriate institutional responses must involve ongoing dialogue with students and attend to the complexity of their contexts and experiences.

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Publication Details

Journal
Higher Education
Published
2026-07-24
DOI
https://doi.org/10.1007/s10734-026-01730-1
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00

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article

Illuminating complex student realities of artificial intelligence through an entangled pedagogy framework

Yifei Liang, Jack Walton, Michael Henderson, Tim Fawns et al.
Higher Education
Teaching and Learning Programming
article

Illuminating complex student realities of artificial intelligence through an entangled pedagogy framework

Yifei Liang, Jack Walton, Michael Henderson, Tim Fawns, Kelly Matthews, Glenys Oberg, Thomas Corbin, Margaret Bearman, Jan McLean
article en

Abstract

Abstract The emergence of widely available Generative AI (GenAI) in higher education demands nuanced responses that acknowledge the diversity of student contexts, conditions, practices and perspectives. Developing such nuanced responses requires attention to how students themselves experience and negotiate GenAI within the complexity of their everyday study and lives. In this paper, we focus on three participants from a broader set of focus groups to explore their perspectives on GenAI in relation to their studies, identities, and practices, and how contextual and personal factors shape these views. Using Fawns’ (2022) entangled pedagogy framework to guide analysis, we illuminate some complex entanglements that characterise students’ engagement with GenAI. Our analysis reveals how our students’ views were entangled with conceptions of learning, institutional governance, moral positions, and contextual considerations. By focusing on just three participants, we were able to examine the tensions and contradictions in how their relationships with GenAI were shaped in relation to circumstances, purposes, values, practices, identities, institutions, educators, peers, and perceived future trajectories. Our results suggest that meaningful action requires creating collaborative spaces where students’ voices are heard, fostering approaches that acknowledge the sociomaterial nature of learning with emerging technologies. We therefore argue that appropriate institutional responses must involve ongoing dialogue with students and attend to the complexity of their contexts and experiences.

Higher Education
University of Technology Sydney (AU), Deakin University (AU), Queensland University of Technology (AU), The University of Queensland (AU), University of Southern Queensland (AU), Monash University (AU)
University of Technology Sydney, Deakin University, Monash University
Openalex Percentile: Top 4%
Teaching and Learning Programming
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