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The Algorithmic Learner: How Platform Logic Shapes Gen-Z's Attention, Motivation, and Wellbeing

Publicada
Servidor
Preprints.org
DOI
10.20944/preprints202512.1589.v1

As learning settings become more intermediated by algorithm-based platform, a new “invisible curriculum” takes shape that organizes the cognitive and affective substrates of Generation Z. This paper examines the way in which the embedded logic of these platforms (that is, the way they are designed to be quantified, to predictively recommend, and to engage) shapes attention, motivation and psychosocial well-being. Drawing on educational psychology and platform studies, we apply a mixed-method approach that combines critical architectural analysis, survey data (n=XXX), and semi-structured interviews (n=XX), with interpretation through the conceptual perspectives of attentional economics and digital nudge theory. Results suggest that algorithmic curation sustains short-cycle attention and prioritises fast interaction over slow consumption, cultivating motivational states that are attentive to notifications and micro-feedback loops. Adaptive elements can tailor learning experiences, but also promote compulsive checking, emotional volatility, and a decrease in intrinsic motivation for lengthy undertakings underscoring a fundamental tension between platform effectiveness and cognitive health. They go on to say that “platform logics” infuse educational institutions as a force that impacts them systemically. The talk will make the case for, and present specific design principles and policy mechanisms for, healthier, learner-centred digital ecosystems that re-enable agency of the algorithmic learner.

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