AI-Enabled Personalized Cybersecurity Education for Adolescents: Deep Learning Methods and Impact Assessment

In response to the escalating cybersecurity threats targeting adolescents, this study proposes an innovative artificial intelligence (AI)-driven framework designed to transform conventional cybersecurity education through personalized and adaptive learning. The system integrates convolutional neural networks (CNN), long short-term memory (LSTM) networks, and natural language processing (NLP) to establish a multimodal behavioral perception mechanism, generating a cybersecurity behavior vector that quantifies vulnerabilities in areas such as phishing susceptibility and privacy protection. A Transformer-based cognitive mapping engine aligns these behavioral features with a structured knowledge graph built from extensive cybersecurity databases, enabling dynamic generation of personalized learning units—including interactive comics, gamified phishing challenges, and AR-based scenarios—optimized via a contextual multi-armed bandit algorithm to enhance long-term retention while minimizing cognitive load. A 12-week randomized controlled trial with 412 middle school students demonstrated that the AI-enabled approach significantly outperformed traditional methods, yielding a 32% increase in knowledge retention, a 2.1-fold improvement in threat detection accuracy, a 28% rise in self-reported security behaviors, and a 41% boost in engagement metrics. The results validate the efficacy of adaptive, data-rich interventions in fostering sustainable cybersecurity habits. The study concludes with policy recommendations for integrating AI-driven personalized education into national cybersecurity strategies, promoting cross-departmental collaboration for resource sharing, and establishing ethical guidelines for equitable and transparent educational AI systems.