This work is situated in the field of philosophy of education, and also relates to the field of educational technologies. The thesis seeks a philosophical understanding of the impacts of machine learning in education. To do so, it addresses the assumptions underlying machine learning in conjunction with the premises underlying the conception of human learning that derive from enactivism. It is argued that the arrival of machine learning in education found fertile ground in which the cognitivist paradigm still prevails, a situation that is rather fruitful for technologies based on data and neural networks to thrive. The thesis demonstrates that this paradigm, however, has been challenged by other research approaches that are dedicated to the human mind, among which enactivism is emphasized. The fundamental theoretical underpinnings of autopoietic enactivism are explained, as well as how they unfold in assumptions for a notion of human learning that is embodied and essentially oriented towards the coupling of the human organism with the world and with other agents. Particular attention is drawn to the impacts of machine learning on the autonomy of the cognizer, which, from the perspective of enactivism, can only exist and be maintained in exchanges with the environment and with those who inhabit and shape this environment. It is shown that for algorithmic technologies to be suited to an enactivist conception of cognition and learning a greater appreciation of the body in learning is necessary, as well as intersubjectivity, since the connections between cognitive agents are not conceived as optional articulations, but as an element that is at the core of human cognitive activity and from which this activity emerges.