Another major shift in game scripting concerns live interaction, where static dialogue trees are giving way to runtime language generation for non-player characters, companions, and role-play systems. Traditional scripting excels at reliability, localization, and authorial voice because every branch is curated, but it becomes brittle when players seek open-ended expression beyond predefined options. Recent systems suggest a different possibility. Zhao et al. (2024) presented NarrativePlay, which uses large language models to let users role-play inside narrative settings while interacting with characters whose responses are shaped by extracted personality traits and contextual story information, and Park et al. (2023) showed more broadly that generative memory and reflection mechanisms can support believable ongoing social behavior rather than isolated conversational turns. Emerging design experiments such as AI-native narrative games build on this same premise: script no longer means only prewritten lines, but an evolving policy for dialogue, memory, and scene progression. Yet this flexibility introduces problems that classical scripting had largely solved, including tone drift, safety failures, excessive verbosity, inconsistent characterization, and the erosion of carefully tuned pacing. The research gap is therefore not merely better dialogue quality but better dialogue governance. Runtime-generated speech must remain aligned with lore, quest state, cultural expectations, moderation policy, and the gameplay economy of attention, all while preserving the illusion of spontaneity. Future research should emphasize role-sensitive prompting, memory schemas, retrieval from canonical lore documents, and mixed-initiative writing tools that allow authors to specify style envelopes and red lines around generative behavior. If those mechanisms mature, AI may transform live dialogue from a risky novelty into a robust scripting layer for socially responsive game worlds; if they do not, open-ended NPC conversation will remain compelling in demos but unreliable in production-scale design (Zhao et al., 2024; Park et al., 2023; Sun, 2024).
References1. Zhao, R., Zhang, W., Li, J., Zhu, L., Li, Y., He, Y., & Gui, L. (2024). NarrativePlay: Interactive narrative understanding. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations.
2. Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST 2023).
3. Sun, Y. (2024). 1001 Nights: AI-native narrative game driven by large language models. In Abstract Proceedings of the DiGRA 2024 Conference.
S. M. Monowar KayserLecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/
