El material Montessori: de la vida práctica a la mente matemática

Blanco, Carmen Sanchidrián

doi:10.17227/pys.num58-17295

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…48), that sensorimotor system and cognition are intertwined, and that knowledge, concepts and higher cognitive functions are brought about by sensory and motor activities, education occurs through the design and organisation of the materiality of learning (e.g., Montessori, 2023;Ahlquist, 2023;Saha, & Adhikari 2023b; see also: Lawrence & Staehli, 2023;Müller & Schneider, 2002). Educational materials and physical classrooms are designed by Montessori herself as affordances for perceptual and active exploration and for driving attention (Moretti, 2021;Lillard, 2017;Catherine et al, 2020;Ponticorvo et al, 2019;Müller & Schneider, 2002;Frierson, 2022;Montessori, 2023;Denervaud, 2023;Laski & Wang, 2023). The MM can be structured around four core ideas that, might be worth noting here, are the same and with roughly the same meaning as the related adopted by the AIF: intrinsic motivation, attention, precision, and errorcontrol (Rathunde, 2009;Lillard, 2019;Demangeon et al, 2023).…”

Section: Active Inference Learning In a Montessori Classroommentioning

confidence: 99%

Active Inference Goes to School. The Importance of Active Learning in the Age of Large Language Models

Di Paolo,

White,

GUÉNIN--CARLUT

et al. 2024

Preprint

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Human learning essentially involves embodied interactions with the material world. But our worlds now include increasing numbers of powerful and (apparently) disembodied generative AIs. In what follows we ask how best to understand these new (somewhat “alien”, because of their disembodied nature) resources and how to incorporate them in our educational practices. We focus on methodologies that encourage exploration and embodied interactions with ‘prepared’ material environments, such as the carefully organised settings of Montessori education. Using the Active Inference Framework, we approach our questions by thinking about human learning as epistemic foraging and prediction error minimization. We end by arguing that generative AIs should figure naturally as new elements in prepared learning environments by facilitating sequences of precise prediction error enabling trajectories of self-correction. In these ways we anticipate new synergies between (apparently) disembodied and (essentially) embodied forms of intelligence.

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