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Research Article

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

Synthesis: Fawns, Bearman, Corbin, Henderson, Walton, Liang, McLean, Oberg & Matthews (2026) offer a theoretically informed qualitative analysis of how three university students (John, Vivian, Howard) negotiate generative AI within the "messy" realities of everyday study and life. Using Fawns' (2022) entangled pedagogy framework — grounded in Barad's sociomaterial/relational ontology — the study shows that GenAI engagement is inseparable from students' overlapping identities (learner, future professional, employee, parent), beliefs about learning, emotional responses, pragmatic constraints, trust relations, and moral judgements about responsibility, authenticity, and fairness. Student positions on GenAI are multiple, ambiguous, provisional, contextual, and sometimes internally conflicting. Contrasting with a sector fixated on staff development, Assessment redesign, and student compliance (e.g., AI detection), the authors argue policy and guidance should be co-designed with students and attend to lived realities rather than treating GenAI as a problem for universities. Students' positionings, they contend, are often more thoughtful and ethically attuned than public discourse suggests.

Key Findings

  • Entangled pedagogy as an analytic lens. The study applies Fawns' (2022) framework, which treats technologies as integral to, rather than separable from, educational activity. Drawing on sociomaterial scholarship (Barad, Fenwick, Orlikowski), the authors treat GenAI engagement as emerging from shifting configurations of social, material, institutional, and personal relations — resisting instrumental or solutionist framings that abstract AI from context.
  • Students hold multiple, overlapping identities. John is simultaneously a postgraduate student, a father, and a mathematics educator; Vivian is a neurodivergent (ADHD) art history student; Howard is an arts/psychology major working at a hardware store. Each negotiates GenAI across the intersections of these roles rather than as a discrete "student" decision.
  • Positions are provisional, ambiguous, and sometimes conflicting. John criticises peers "getting away with using AI" yet also criticises universities for prohibiting it; Vivian rejects GenAI as inauthentic yet admits "AI sits across a whole lot of platforms... you can't avoid it entirely"; Howard trusts GenAI "to be untrustworthy" and uses it in ways that "technically do" breach rules while believing he upholds his personal standard of academic integrity.
  • Emotion, trust, and morality are entangled with pragmatics. GenAI use is negotiated amid guilt, fear, frustration, and pragmatic pressure (e.g., Howard submitting assignments "with 2 min to spare," his fear of detection and "harsh consequences"). Decisions blend personal integrity, institutional expectations, trust relations with institutions/peers/technologies, and concerns about future employability and the "human element" of learning.
  • Sector approaches mis-frame the issue. Dominant responses — staff development, assessment redesign, compliance regimes, AI detection — rely on students complying with policy despite evidence their views diverge from educator expectations. Policy does not translate cleanly into practice, because GenAI is entangled with ethics, identities, relationships, aspirations, and external pressures.

Implications for AI in Education

The paper argues meaningful institutional response requires creating collaborative dialogic spaces where students' voices are heard and educational tensions are made visible "by design" (a seamful approach), rather than resolving complexity through top-down rules or surveillance. It positions students as co-interpreters and co-designers — not passive recipients of policy or potential rule-breakers — and draws on the students-as-partners literature (Matthews et al.) to argue for cultures of openness, honesty, and vulnerability over adversarial behavioural control. The authors caution that challenges of equity, diversity, and inclusivity make it harder to ensure a "level playing field" when students must make their own contextual judgements. They call for moving beyond binary framings of AI as harmful or beneficial, and beyond "AI literacy" conceived purely as technical competency, toward fostering reflective, ethical, context-sensitive judgement. Notably, they frame entangled pedagogy as a flexible, reflexive starting point for analysis — with the authors using Barad's notion of agential cuts as a "viewfinder" to bound what is foregrounded — rather than an expandable model that encompasses everything. This connects to themes of institutional governance, trust relations, student wellbeing, and equity in the knowledge base.

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Citation

Fawns, T., Bearman, M., Corbin, T., Henderson, M., Walton, J., Liang, Y., McLean, J., Oberg, G., & Matthews, K. E. (2026). Illuminating complex student realities of artificial intelligence through an entangled pedagogy framework. Higher Education.

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