Fostering Algorithmic Literacy in Education: Navigating News Ecosystems for Critical Media Understanding
DOI:
https://doi.org/10.5281/zenodo.16388403Keywords:
Algorithmic Curation, Media Literacy, Polarization, Filter Bubble, Algorithmic LiteracyAbstract
In an era of algorithmically curated news feeds, the interplay between technology and human behavior is transforming global information consumption. This study systematically reviews literature from 2015 to 2024, examining algorithms’ dual role as enhancers of personalization and drivers of polarization. It investigates how algorithmic bias influences news diversity, the effects of algorithmically driven news exposure on polarization, and the potential of media literacy to mitigate these impacts. The findings reveal a complex relationship between algorithmic curation, user behavior, and polarization, often exacerbated by system opacity. While algorithms can broaden exposure to diverse perspectives, they frequently reinforce existing beliefs through filter bubbles and echo chambers. Media literacy emerges as a vital tool, equipping individuals to critically engage with content and challenge biases. Addressing a growing research gap, this study explores the intricate dynamics between algorithmic personalization, polarization, and media literacy, proposing an educational framework to equip learners for AI-driven news environments. The proposed framework interconnects algorithmic curation, news exposure, user agency, media literacy, and polarization, emphasizing their cyclical dynamics. This research calls for algorithmic transparency, cross-cultural media literacy programs, and targeted studies in underrepresented regions, offering actionable pathways to support healthier public discourse through including algorithmic literacy in education.
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