To equip new counsellors at a Dutch child helpline with the needed counselling skills, the helpline uses role-playing, a form of learning through simulation in which one counsellor-in-training portrays a child seeking help and the other portrays a counsellor. However, this process is time-intensive and logistically challenging-issues that a conversational agent could help address. In this paper, we propose an initial design for a computer agent that acts as a child help-seeker to be used in a role-play setting. Our agent, Lilobot, is based on a Belief-Desire-Intention (BDI) model to simulate the reasoning process of a child who is being bullied at school. Through interaction with Lilobot, counsellors-in-training can practise the Five Phase Model, a conversation strategy that underpins the helpline’s counselling principle of keeping conversations child-centred. We compared a training session with Lilobot to a text-based training, inviting experienced counsellors from the Dutch child helpline to participate in both sessions. We conducted pre- and post-measurement comparisons for both training sessions. Contrary to our expectations, the results show a decrease in counselling self-efficacy at post-measurement, particularly in Lilobot’s condition. Still, the counsellors’ qualitative feedback indicated that, with further development and refinements, they believed Lilobot could potentially serve as a useful supplementary tool for training new helpline counsellors. Our work also highlights three future research directions for training simulators in this domain: integrating emotions into the model, providing guided feedback to the counsellor, and incorporating Large Language Models (LLMs) into the conversations.

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.

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.

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.

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).

Increased levels of user control in learning systems is commonly cited as good AI development practice. However, the evidence as to the effect of perceived control over trust in these systems is mixed. This study investigated the relationship between different trust dimensions and perceived control in postgraduate student burnout support chatbots, and modelled the moderating factors therein. We present an in-between subject controlled experiment using simulated therapy-goal learning to study the effect of perceived control (as manipulated by feedback incorporation) on perceived agent benevolence, competence, and trust. Our results showed that perceived control was moderately correlated with benevolence (r = 0.448, BF10 = 7.150), and weakly correlated with competence and trust.

Virtual patients (VPs) offer an affordable and feasible method to train individuals when compared to patient-actors. They can provide training on communication skills, such as motivational interviewing and conflict resolution, and often facilitate a change in patients’ thinking and emotional states. However, few studies have focused on VPs with cognitive and emotional states and internal schema or rules that govern them. In this research, we aim to design and empirically study a VP training system with a mental model to enrich the interactions and training. With such a model, the learning environment has the potential to generate valuable feedback and guidance for the learner based on the states of the VP. We started by examining systems aimed at training individuals with a virtual agent that simulates a person in a social situation (e.g., a virtual customer to train salespersons). We developed an architecture for these systems, defined the current interaction approaches, and will discuss the main aspects of the training system architecture and various technological approaches. Here, we consider the potential solutions proposed in adjacent virtual-agent domains. Based on our findings, we will model the VP’s reasoning to improve trainees’ communication skills and investigate how helpful feedback and guidance could be generated from these mental models at run time. The main contribution of our work will be the build of an empirically grounded virtual patient with a mental model as a training system.

The perception of warmth and competence in others influences social interaction and decision making. Virtual agents have been used in many domains including serious gaming and training. In this work we study the effect of warmth expressed in the behavior of a virtual agent on a human-agent negotiation. We design and conduct an experiment where participants negotiate with two versions of the same agent displaying varying levels of warmth. The results show that humans are more satisfied with the warm agent, are more willing to renegotiate with it, would recommend the agent more to their friends and had a better interaction experience, even though there is no difference in negotiation outcome (utility, agreement or rounds needed). While studies have shown effects of emotional displays on negotiation and collaboration, this is - to our knowledge - the first time that a clear effect of behavioral style is shown on the post-hoc appraisal of a human-agent collaboration, in our case a negotiation.

Burning fossil fuels is a big part of our heat production. Since this process is both non-renewable and polluting, finding other options is important. A clean and underutilized alternative is geothermal energy. However, it is often not considered due to sheer ignorance or misconceptions. HotPipe is a serious game designed to alleviate these problems, particularly among youth populations. Players control a drill to create geothermal wells solving a variety of puzzles, which introduce relevant cases for geothermal heating and show what geothermal wells are made of. The game focuses primarily on conveying the concepts of water circulation, relation between temperature and depth, androck type proprieties. From our game evaluation, players revealed a solid improvement on their geothermal energy knowledge.