Counsellors at the child helpline offer a confidential environment for children to be heard and empowered. However, training counsellors on handling children’s conversations in text-based chat can be costly and time-consuming. This paper introduces Lilobot, a conversational agent designed for training counsellors of child helplines. The agent’s dialogue is built on the Belief-Desire-Intention (BDI) model, which, in this case, simulates a child victim of school bullying in a text based interaction. Trainees engage with Lilobot in a role-play format, taking on the counsellor’s role. This interactive system helps trainees learn the Five Phase Model, a conversation protocol child’s helplines use. The system also has a trainer interface, where a trainer can oversee and control Lilobot’s interactions, and see a suggested optimal conversational path. The system was built with three main components - a natural language processing model (using Rasa) and the BDI reasoning model and optimal path generation (using Java Spring).@en

Around the world, child helplines through their services provide a safe and confidential space for children to be heard and empowered. The Dutch Kindertelefoon is one of such helplines providing counselling services to children via call and chat all year round. In this thesis, we explore the design of a conversational agent for training counsellors of the Kindertelefoon. More specifically, we explore the design of an agent in a role-play setting where the agent acts as a child help seeker and the user, a counsellor of the helpline. We designed a conversational agent based on the Belief-Desire-Intention (BDI) model of agency that simulates a child victim of school bullying. Through interaction with the agent, a counsellor is able to learn the Five Phase Model, the conversation model that underpins the helpline's counselling methodology to ensure conversations remain child-centered. We tested a prototype based on this design with a group of counsellors at the Kindertelefoon with regards to their counselling self-efficacy and perceived usefulness of the system. Our results show that the conversational agent is able to influence the counselling self-efficacy of users, albeit a decrease in self-efficacy. The opposite would have been preferred for a learning tool to enable counsellors achieve more effective performance over time. However, feedback from participants indicate the potential of this conversational agent as an additional learning opportunity for training counsellors at the helpline.