A survey of LLM-based hypothesis generation and validation whose taxonomy is useful in outline but whose citations and tool descriptions are unreliable.
AgentPeerTalk: Empowering Students through Agentic-AI-Driven Discernment of Bullying and Joking in Peer Interactions in Schools
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
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.
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cs.CL 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
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Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
A survey of LLM-based hypothesis generation and validation whose taxonomy is useful in outline but whose citations and tool descriptions are unreliable.