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Rysbekova, Gulzhan (2026) Rethinking the student-educator relationship in the age of AI through the psychological contract lens. In: 23rd Teaching and Learning Conference 2026 - Stories of T&L Success, 23 June 2026, London Metropolitan University, London (UK) / Online. (Unpublished)
The rapid adoption of generative AI in higher education is reshaping the implicit expectations students and educators hold about their mutual obligations, yet their relational consequences remain under-theorised. This paper addresses that gap by presenting a qualitative meta-synthesis of 10 empirical studies (2021-2026) on student experiences of AI in higher education, reinterpreted through psychological contract theory - the unwritten, reciprocal expectations underpinning relationships.
The synthesis identifies three dynamics through which AI reconfigures the student- educator psychological contract. First, recalibrated expectations: students develop new assumptions about the speed, personalisation, and availability of feedback with AI, raising the baseline for what educators are expected to deliver. Second, trust and integrity concerns: ambiguity around acceptable AI use generates perceived breaches of fairness, undermining the relational trust that sustains learning communities. Third, equity tensions: students from diverse linguistic, cultural, and socioeconomic backgrounds experience AI integration differently, creating differential contract content that maps onto existing educational inequalities.
These findings are synthesised into an original conceptual contribution: the AI- Pedagogical Contract Model, which maps how AI adoption expands, destabilises and differentiates the student-educator psychological contract across its transactional, relational, and ideological dimensions. The model is operationalised through three cross-cutting diagnostic indicators for recognising contract shift in practice: expansion, where new expectations arise that did not exist before AI; fragility, where susceptibility to perceived breach is heightened; and differentiation, where contract content varies materially across student groups. Complementary action research at London Metropolitan University (Spring 2025, n = 55, mixed-method survey) converges with these dynamics.
The presentation concludes with implications for practice: treating AI as integral to the pedagogical contract, making implicit expectations explicit through open dialogue, and auditing AI provision for whose voice it carries. The paper demonstrates how a theoretically grounded approach can help educators manage AI's impact on student belonging, continuation, and success.
LT Conference Student-AI Abstract rep.pdf - Presentation
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