  Addressing school bullying effectively and promptly is crucial for the mental
health of students. This study examined the potential of large language models
(LLMs) to empower students by discerning between bullying and joking in school
peer interactions. We employed ChatGPT-4, Gemini 1.5 Pro, and Claude 3 Opus,
evaluating their effectiveness through human review. Our results revealed that
not all LLMs were suitable for an agentic approach, with ChatGPT-4 showing the
most promise. We observed variations in LLM outputs, possibly influenced by
political overcorrectness, context window limitations, and pre-existing bias in
their training data. ChatGPT-4 excelled in context-specific accuracy after
implementing the agentic approach, highlighting its potential to provide
continuous, real-time support to vulnerable students. This study underlines the
significant social impact of using agentic AI in educational settings, offering
a new avenue for reducing the negative consequences of bullying and enhancing
student well-being.
