AgentPeerTalk: Empowering Students through Agentic-AI-Driven Discernment of Bullying and Joking in Peer Interactions in Schools
It addresses bullying in schools to protect student mental health, but is incremental as it applies existing LLMs to a new domain.
This study tackled the problem of school bullying by using large language models (LLMs) to discern between bullying and joking in peer interactions, finding that ChatGPT-4 showed the most promise with context-specific accuracy after implementing an agentic approach.
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.