From mitigation to exploration: Reimagining teaching and learning with generative AI in higher education

Generative artificial intelligence (GenAI) forces a reconfiguration of pedagogy and assessment in higher education. This conceptual paper draws on Deleuze and Guattari’s rhizome to contrast hierarchical educational practices with networked, rhizomatic learning. While GenAI outputs appear rhizomatic, they function as tracing mechanisms that recombine statistical patterns rather than generate novel cartographies. This creates a productive asymmetry that educators can exploit. By foregrounding embodied, situated, and processual dimensions of inquiry, teachers can design tasks and assessments that GenAI cannot easily simulate. The paper proposes concrete, transferable pedagogical strategies, including process portfolios, rupture-based pivots, and participatory activities. Shifting away from surveillance and policing AI, this framework repositions teachers as cartographer-guides. It fosters critical engagement, treating GenAI as just one node within a heterogeneous learning network, ultimately moving higher education from AI mitigation towards meaningful exploration.