NAVIGATING THE ALGORITHM FEED: A CRITICAL MEDIA LITERACY FRAMEWORK FOR THE AI ERA

The proliferation of algorithmic content curation on social media has intensified the spread of AI-driven misinformation, deepfakes, and polarizing narratives. Yet, most formal media literacy curricula remain rooted in static, text-based evaluation techniques that do not account for the speed, scale, and algorithmic opacity of contemporary digital platforms. This paper addresses the critical gap between existing media literacy frameworks and the real-time, user-generated nature of online information environments. Drawing on Kellner and Share's Critical Media Literacy as a theoretical lens, the study argues that traditional approaches focused on verifying source credibility or detecting bias in static messages are insufficient for navigating dynamic, algorithmically curated feeds where content is personalized, ephemeral, and often stripped of provenance cues. Through a conceptual analysis of recent cases involving AI-generated images, viral disinformation campaigns, and echo chamber effects, the paper identifies five specific limitations of current educational models: (1) lack of attention to algorithmic amplification, (2) minimal training on synthetic media detection, (3) passive consumption rather than participatory critique, (4) decontextualized skill exercises, and (5) insufficient focus on affective manipulation. In response, the paper proposes strategic adaptations that integrate platform literacy, real-time verification heuristics, and critical interrogation of recommendation engines. The findings suggest that without updating media literacy for the AI era, even well-educated users remain vulnerable to computational propaganda. This research offers actionable guidelines for educators, curriculum designers, and policymakers seeking to close the literacy gap in high-speed, low-transparency digital ecosystems.