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We investigate the use of generative AI to create complex speech-enabled conversational characters for immersive and safe interactive narratives in video games. To explore this, we develop a murder-mystery game in Minecraft, featuring large language model (LLM)-driven non-player characters (NPCs) that support open-ended, voice-based interaction. By combining GPT-4o with ASR and TTS modules, players can converse with characters, and attempt to solve a mystery through natural dialogue. Prompt engineering techniques are applied to enhance character consistency, safety, and narrative depth. Evaluations show improved performance in terms of character correctness, dialogue coherence, and safety – for example improving safety and character consistency significantly (p = 0.00195). The project materials are available at [github] . Demo video available at: [video] .

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Designing and Evaluating Interactive Narratives with Generative AI: LLM-Driven NPCs for a Minecraft Murder Mystery

  • 会议论文

  • 发表日期：

    2026年

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  • Jiangao Ma,
  • Junlin Wu,
  • Hanjing Wang,
  • Shengyu Lu,
  • Yi Ding,
  • Ruotong Peng,
  • Cheng Peng,
  • Yusheng He,
  • Ruoxuan Liu,
  • Yifan Zheng,
  • Lin Sun,
  • Oliver Lemon

摘要

We investigate the use of generative AI to create complex speech-enabled conversational characters for immersive and safe interactive narratives in video games. To explore this, we develop a murder-mystery game in Minecraft, featuring large language model (LLM)-driven non-player characters (NPCs) that support open-ended, voice-based interaction. By combining GPT-4o with ASR and TTS modules, players can converse with characters, and attempt to solve a mystery through natural dialogue. Prompt engineering techniques are applied to enhance character consistency, safety, and narrative depth. Evaluations show improved performance in terms of character correctness, dialogue coherence, and safety – for example improving safety and character consistency significantly (p = 0.00195). The project materials are available at [github] . Demo video available at: [video] .