Purpose Generative AI (GenAI) in education brings renewed attention to learner autonomy – that is, whether learners can think and act independently. GenAI offers the promise of learning efficiency and personalization, while raising questions about its alignment with nurturing autonomous learners. In this paper, we present a theoretical framework to investigate the relationship between GenAI and learner autonomy, to guide the design of educational environments that are safe and autonomy-supporting. Design/methodology/approach Our paper explores the multifaceted nature of autonomy across the cognitive, philosophical, political and computing fields, connecting theories such as self-determination theory with reflections on machine autonomy. Leveraging Latour's Actor-Network Theory, our framework aims to elucidate how autonomy is distributed between human and non-human actors in educational environments. Findings Our main contribution is the process of “autonomy budgeting”, viewing autonomy as a resource that is allocated and traded off between an ensemble of actors. Autonomy budgeting works as a guiding conceptual tool for researchers, educators, curriculum designers and policymakers to assess and manage the autonomy trade-offs involved in integrating GenAI into educational environments. Research limitations/implications By re-centering the learner's agency and capacity for self-regulation, autonomy budgeting provides a way to conceptualize and operationalize autonomy within AI-mediated education, and to navigate the complex interplay between human and machine agency in education. Originality/value Our framework develops reflections on the socio-technical nature of educational processes, where technologies act as co-participants rather than neutral tools. Autonomy in education, becomes a multifaceted construct that spans (human) cognitive, epistemic and political domains, and must be considered vis-a-vis varying degrees of machine autonomy.
Balzan et al. (Tue,) studied this question.