Improving media literacy through neural networks: a novel artificial intelligence-based approach

In the digital information era, media literacy has emerged as an essential competence to critically assess the credibility, bias, and intent of online content. As the volume of misinformation and manipulated media continues to grow, traditional educational approaches struggle to keep pace. This paper presents a novel artificial intelligence-based framework that utilizes neural networks to enhance media literacy, especially among students and digital content consumers. The proposed model employs deep learning techniques, including convolutional and recurrent neural networks, to detect fake news, assess source credibility, and classify media content based on linguistic and visual features. Experimental evaluations using real-world datasets demonstrate significant improvements in users' ability to identify biased or false content. The findings highlight the potential of AI-powered tools to support scalable, adaptive, and personalized media literacy education. Visual analytics, prototype implementations, and source code are included to support reproducibility and future research.