The integration of artificial intelligence into development research methodologies presents unprecedented opportunities for addressing persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing from an empirical implementation in Sri Lanka's Sinhala-speaking communities, this paper presents an empirically grounded methodological framework designed to transform participatory development research, situated in the challenging multilingual context of Sri Lanka's flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, this framework deploys a multi-agent system architecture that redefines how data collection, analysis, and community engagement are conducted in linguistically and culturally diverse research settings. This structured agent-based approach enables participatory research that is both scalable and responsive, ensuring that community perspectives remain integral to research outcomes. Field experiences reveal the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, offering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research agenda advocates for AI-driven participatory research tools that maintain ethical considerations, cultural respect, and operational efficiency, highlighting strategic pathways for deploying AI systems that reinforce community agency and equitable knowledge generation, potentially informing broader research agendas across the Global South.

翻译：将人工智能融入发展研究方法论，为解决参与性研究中长期存在的挑战提供了前所未有的机遇，尤其是在南亚等多语言地区。本文基于在斯里兰卡僧伽罗语社区的实证实践，提出一个扎根于实证的方法论框架，旨在变革参与性发展研究。该框架植根于斯里兰卡易发洪水的尼尔瓦拉河流域这一充满挑战的多语言环境，超越了传统的翻译与数据收集工具，部署了一种多智能体系统架构，重新定义了在语言文化多元的研究场景中进行数据收集、分析和社区参与的方式。这种结构化的基于智能体的方法实现了兼具可扩展性与响应性的参与性研究，确保社区视角始终是研究成果的核心组成部分。实地经验揭示了基于LLM的系统在解决资源有限地区发展研究中长期存在问题的巨大潜力，在提升包容性方面同时实现了量化效率与质性改进。在更广泛的方法论层面，本研究议程倡导开发遵循伦理考量、文化尊重与操作效率的AI驱动型参与性研究工具，着重阐明了部署AI系统的战略路径——这些系统应能强化社区能动性与公平的知识生成，并为全球南方更广泛的研究议程提供参考。