mlsanityData to 7 Oct 2026

AgentPeerTalk: Empowering Students through Agentic-AI-Driven Discernment of Bullying and Joking in Peer Interactions in Schools

arXiv 2408.01459 ↗First posted 27 Jul 2024cs.CY cs.AI cs.CLcited by 1

On the map: LLM agents and tool use → Educational agents and math problem solving

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 24 of its 34 references that are in the map. First posted on arXiv 27 Jul 2024.

Builds on

24 of 34 references.

References · 24

  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
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12 more
  1. 2023OpenAGI: When LLM Meets Domain Expertscited by 196
  2. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  3. 2019RUBi: Reducing Unimodal Biases in Visual Question Answeringcited by 183
  4. 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
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  12. 2023Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Modelscited by 9

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

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