Exploring the Potential of ChatGPT in Developing a Virtual Assistant for Prevention and Support in Cases of School Bullying
Bullying is a global problem that negatively affects students, their families, and communities. This study addresses the creation and evaluation of an artificial intelligence (AI)-based chatbot to provide emotional support to student victims of bullying, using a combination of exploratory and descr...
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| Format: | Article |
| Language: | English |
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Centro Latinoamericano de Estudios en Informática
2025-08-01
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| Series: | CLEI Electronic Journal |
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| Online Access: | https://clei.org/cleiej/index.php/cleiej/article/view/711 |
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| Summary: | Bullying is a global problem that negatively affects students, their families, and communities.
This study addresses the creation and evaluation of an artificial intelligence (AI)-based
chatbot to provide emotional support to student victims of bullying, using a combination of
exploratory and descriptive approaches. A chatbot was developed on the Telegram messaging
platform, integrating the ChatGPT API to improve response generation. A topic- and equitysensitive design was implemented, and conversation logging and harassment detection
functionality was enabled. The research focused on the technical functionality of the chatbot
and its ability to provide emotional support and guidance. The chatbot proved to be capable of
providing basic emotional support and guidance to users. The integration of ChatGPT
enriched the consistency and contextualization of responses. Intuitive interaction was
achieved through Telegram, providing relevant and contextually appropriate responses.
Although this research focused on the technical functionality of the chatbot, its
implementation in students and data analysis of user interactions represent important future
steps. The chatbot has the potential to empower victims of bullying and improve school
safety. In addition, this research contributes to the creation of a new tool for collecting and
analyzing data on bullying, improving early detection and prevention.
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| ISSN: | 0717-5000 |