Designing Meaningful AI-Generated Dialogue: The Behaviour-Driven Conditional Prompting Framework for Serious Games

Many serious games use dialogue between players and non-player characters (NPCs) to enhance learning.However, designing appropriate dialogue is often time-consuming for game developers. Recent advancements in artificialintelligence, particularly Large Language Models (LLMs), have made open-ended dialogue with virtual characters feasible,though managing it in educational contexts remains a significant challenge. This study explores how game designers canguide open-ended dialogue powered by LLMs to create meaningful educational conversations. Expert interviews and areview of existing approaches to implementing open-ended conversation in games led to the formulation of designrequirements for a new framework. Based on these insights, the Behaviour-Driven Conversational Prompting (BDCP)framework was developed. This framework offers practical guidance for designers to create scenarios where behaviourallearning objectives are achieved through structured dialogue. It combines lock-and-key narrative design, dynamic characterprompts that dictate LLM-generated responses, and behaviour analysis prompts that assess player interactions. To validatethe framework, a functional prototype called ‘Detective Duck’ was created. This detective-style 'whodunit' game has playerssolve crimes through open-ended conversation with AI-driven characters. Players encounter challenges such as persuadinghesitant witnesses, or verifying alibis. These challenges can only be solved by demonstrating reasoning and conversationalstrategies relevant for detectives such as lateral thinking and persuasion. Upon demonstrating these behaviours, characterprompts can be dynamically adjusted, ensuring that - only then - players receive key clues needed to advance the narrative.The framework and prototype were evaluated against the established design requirements and through expert interviews.Results were largely positive, indicating that the BDCP framework supports meaningful, open-ended dialogue aligned witheducational objectives. However, some inconsistencies in dialogue coherence and adherence to designer intent were noted.Future work will focus on refining narrative consistency and enhancing adherence to designer intent by fine-tuning thelanguage model, integrating AI-driven player feedback, and incorporating other game mechanics into the framework. Theseimprovements would further strengthen the BDCP framework as a tool for designing serious games centered around openendedconversation.