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Use of the LLAMA2 Tool for the Early Detection of Bullying in Seventh-Grade Children

Abstract:

Bullying, or school bullying, is a severe problem that affects the emotional well-being and social development of students, manifesting itself in forms such as physical aggression, verbal abuse, and cyberbullying. Early detection of this behavior is challenging, as it often goes unnoticed by human observers. This study proposes using LLaMA2, a generative artificial intelligence model, to identify early signs of bullying in the verbal interactions of seventh-grade students. By analyzing large volumes of data, LLaMA2 can detect signs of bullying that humans might miss. LLaMA2 will analyze conversations and provide detailed information about the dynamics of bullying, using comparative evaluations of data in statistical graphs. This approach reveals the prevalence and impact of bullying. The findings show specific patterns of bullying, combining quantitative and qualitative analyses for a deeper understanding of the problem. The results highlight that integrating artificial intelligence with traditional methods can improve the detection and management of bullying. The use of LLaMA2, together with statistical analysis, facilitates the design of personalized interventions and the identification of emerging patterns. Cooperation between educators and technology developers is crucial to maximizing impact and creating a safe and healthy environment. Continuous feedback between users and the AI system allows for ongoing adjustments. This comprehensive approach improves bullying management and fosters an inclusive and safe learning environment.