Child helpline training often relies on human-led roleplay, which is both time-and resource-consuming.To address this, rule-based interactive agent simulations have been proposed to provide a structured training experience for new counsellors.However, these agents might suffer from limited language understanding and response variety.To overcome these limitations, we present a hybrid interactive agent that integrates Large Language Models (LLMs) into a rule-based Belief-Desire-Intention (BDI) framework, simulating more realistic virtual child chat conversations.This hybrid solution incorporates LLMs into three components: intent recognition, response generation, and a bypass mechanism.We evaluated the system through two studies: a script-based assessment comparing LLM-generated responses to human-crafted responses, and a within-subject experiment (𝑁 = 37) comparing the LLM-integrated agent with a rule-based version.The first study provided evidence that the three LLM components were non-inferior to human-crafted responses.In the second study, we found credible support for two hypotheses: participants perceived the LLM-integrated agent as more believable and reported more positive attitudes toward it than the rule-based agent.Additionally, although weaker, there was some support for increased engagement (posterior probability = 0.845, 95% HDI [-0.149, 0.465]).Our findings demonstrate the potential of integrating LLMs into rule-based systems, offering a promising direction for more flexible but controlled training systems.

Agent-based training systems can enhance people's social skills. The effective development of these systems needs a comprehensive architecture that outlines their components and relationships. Such an architecture can pinpoint improvement areas and future outlooks. This paper presents ARTES: a general architecture illustrating how components of agent-based social training systems work together. We studied existing systems and architectures for training and tutoring to design ARTES and identify its essential components and interaction characteristics. ARTES comprises two core components: the agent simulation of social situations, and educational elements to provide guided learning. We link ARTES's crucial components to four primary learning theories (behaviourism, cognitivism, social cognitive theory, and constructivism) to illustrate the role of agent simulation and tutoring elements in establishing desired learning outcomes. Furthermore, we map ARTES's components against eight architectures, 43 systems and three tools to indicate the components' relevance, completeness, generalisation, and deployment potential across contexts. In addition to ARTES, the paper also contributes by identifying future improvements and research directions, such as the agent's thinking, tutoring methods, knowledge transfer, and ethical implications. We believe ARTES can help bridge the gap between virtual human simulations and impactful educational learning, offering training system developers desirable features like understandability and adaptability.

Child helplines offer a safe and private space for children to share their thoughts and feelings with volunteers. However, training these volunteers to help can be both expensive and time-consuming. In this demo, we present Lilobot, a conversational agent designed to train volunteers for child helplines. Lilobot’s reasoning is based on the Belief-Desire-Intention (BDI) model, which simulates, for example, a bullied child who contacts the helpline through text. Users engage with Lilobot in a role-play format, taking on the volunteer’s role. Through this system, volunteers can practice applying the Five Phase Model, a conversational strategy helplines use. The training tool includes a trainer interface for monitoring and modifying Lilobot’s interactions. Trainers can also create new conversational scenarios through an authoring tool. An initial evaluation led to enhancements in Lilobot’s knowledge base and intent recognition, addressing the main issues encountered by participants. The components used to implement the system were Java Spring for the BDI model and the authoring tool, Rasa for Natural Language Understanding, PostgreSQL for the database, and Vue.js for the front-end. This tool aims to provide volunteers with consistent, interactive training, enhancing their counselling skills in a controlled environment.