Controlled Yet Natural: A Hybrid BDI-LLM Conversational Agent for Child Helpline Training
Mohammed Al Owayyed, Adarsh Denga, Willem‐Paul Brinkman · 2025
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.