Gamification in the era of chatbots is a novel way to engage users with the chatbot application. When developing a gamified chatbot system, there are factors related to user types (ages, gender and others) that we should consider to effectively integrate the game elements into the chatbot while targeting the right audience. In this study, we discuss the development of an educational chatbot game 'CiboPoli', that's specialised in teaching children about healthy lifestyle through an interactive social game environment. The presented game is based on a paper prototype that we developed to teach primary school students about healthy diet and food waste management. The current approach will be more engaging and pose AI capabilities. This is still a work in progress and we plan to improve its design by incorporating additional components, such as dialog management module, user-specific knowledge module or machine learning module. Future work will be devoted to integrating machine learning to automatically identify learners emotions and provide personalised suggestions. Moreover, we tested the initial prototype with school students and found that it outperforms the paper version. Future work will focus on applying it to other domains and demographics.

An Adaptive Learning with Gamification & Conversational UIs: The Rise of CiboPoliBot

A. S. F. Jumaah;Adolfo Villafiorita
2017

Abstract

Gamification in the era of chatbots is a novel way to engage users with the chatbot application. When developing a gamified chatbot system, there are factors related to user types (ages, gender and others) that we should consider to effectively integrate the game elements into the chatbot while targeting the right audience. In this study, we discuss the development of an educational chatbot game 'CiboPoli', that's specialised in teaching children about healthy lifestyle through an interactive social game environment. The presented game is based on a paper prototype that we developed to teach primary school students about healthy diet and food waste management. The current approach will be more engaging and pose AI capabilities. This is still a work in progress and we plan to improve its design by incorporating additional components, such as dialog management module, user-specific knowledge module or machine learning module. Future work will be devoted to integrating machine learning to automatically identify learners emotions and provide personalised suggestions. Moreover, we tested the initial prototype with school students and found that it outperforms the paper version. Future work will focus on applying it to other domains and demographics.
978-1-4503-5067-9
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/316184
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