As a result of the Boko Haram insurgency, several families had been displaced and many children no longer have access to formal classroom-based education. They are therefore exposed to predators who in turn influence them negatively and corrupt their innocent minds through wrong teachings. The aim of this research is to stop the use of children as suicide bombers by the Boko Haram terrorists in Northern Nigeria through de-radicalization and game-based learning. Children love games –computer games, mobile games... as they are very responsive, and therefore can be deployed in teaching best behaviors by stimulating learners’ involvement. So why not build games using python? In this poster, the author shall be discussing the development of an interactive chatbot trained with the corpus of three local languages (Hausa, Fulfude, and Kanuri) and English (with translations both ways) to stimulate conversations, deliver tailored contents to the users thereby aiding in the detection of radicalization giveaways in learners through data analysis of the games moves and vocabularies. The presentation would show how the chatbot can tell the degree of radicalization in an individual and tailor the contents towards such user's need. The work leveraged on the affordances of mobile devices in Nigeria to build conversational agents that interact with kids living in Internally Displaced Person Camps in North East Nigeria. The chatbot is also being used for security communications and as a natural communications framework for teaching local languages to non-native humanitarian aids workers.