@inproceedings{zhao-etal-2025-enhancing,
title = "Enhancing Participatory Development Research in {S}outh {A}sia through {LLM} Agents System: An Empirically-Grounded Methodological Initiative from Field Evidence in Sri {L}ankan",
author = "Zhao, Xinjie and
Wang, Hao and
Sriwarnasinghe, Shyaman Maduranga and
Tang, Jiacheng and
Wang, Shiyun and
Sugiyama, Sayaka and
Morikawa, So",
editor = "Weerasinghe, Ruvan and
Anuradha, Isuri and
Sumanathilaka, Deshan",
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages",
month = jan,
year = "2025",
address = "Abu Dhabi",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.indonlp-1.13/",
pages = "108--121",
abstract = "The integration of artificial intelligence into development research methodologies offers unprecedented opportunities to address persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing on empirical implementation in Sri Lanka`s Sinhala-speaking communities, this study presents a methodological framework designed to transform participatory development research in the multilingual context of Sri Lanka`s flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, the proposed framework leverages a multi-agent system architecture to redefine how data collection, analysis, and community engagement are conducted in linguistically and culturally complex research settings. This structured, agent-based approach facilitates participatory research that is both scalable and adaptive, ensuring that community perspectives remain central to research outcomes. Field experiences underscore the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, delivering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research advocates for AI-driven participatory research tools that prioritize ethical considerations, cultural sensitivity, and operational efficiency. It highlights strategic pathways for deploying AI systems to reinforce community agency and equitable knowledge generation, offering insights that could inform broader research agendas across the Global South."
}
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<abstract>The integration of artificial intelligence into development research methodologies offers unprecedented opportunities to address persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing on empirical implementation in Sri Lanka‘s Sinhala-speaking communities, this study presents a methodological framework designed to transform participatory development research in the multilingual context of Sri Lanka‘s flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, the proposed framework leverages a multi-agent system architecture to redefine how data collection, analysis, and community engagement are conducted in linguistically and culturally complex research settings. This structured, agent-based approach facilitates participatory research that is both scalable and adaptive, ensuring that community perspectives remain central to research outcomes. Field experiences underscore the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, delivering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research advocates for AI-driven participatory research tools that prioritize ethical considerations, cultural sensitivity, and operational efficiency. It highlights strategic pathways for deploying AI systems to reinforce community agency and equitable knowledge generation, offering insights that could inform broader research agendas across the Global South.</abstract>
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%0 Conference Proceedings
%T Enhancing Participatory Development Research in South Asia through LLM Agents System: An Empirically-Grounded Methodological Initiative from Field Evidence in Sri Lankan
%A Zhao, Xinjie
%A Wang, Hao
%A Sriwarnasinghe, Shyaman Maduranga
%A Tang, Jiacheng
%A Wang, Shiyun
%A Sugiyama, Sayaka
%A Morikawa, So
%Y Weerasinghe, Ruvan
%Y Anuradha, Isuri
%Y Sumanathilaka, Deshan
%S Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F zhao-etal-2025-enhancing
%X The integration of artificial intelligence into development research methodologies offers unprecedented opportunities to address persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing on empirical implementation in Sri Lanka‘s Sinhala-speaking communities, this study presents a methodological framework designed to transform participatory development research in the multilingual context of Sri Lanka‘s flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, the proposed framework leverages a multi-agent system architecture to redefine how data collection, analysis, and community engagement are conducted in linguistically and culturally complex research settings. This structured, agent-based approach facilitates participatory research that is both scalable and adaptive, ensuring that community perspectives remain central to research outcomes. Field experiences underscore the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, delivering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research advocates for AI-driven participatory research tools that prioritize ethical considerations, cultural sensitivity, and operational efficiency. It highlights strategic pathways for deploying AI systems to reinforce community agency and equitable knowledge generation, offering insights that could inform broader research agendas across the Global South.
%U https://aclanthology.org/2025.indonlp-1.13/
%P 108-121
Markdown (Informal)
[Enhancing Participatory Development Research in South Asia through LLM Agents System: An Empirically-Grounded Methodological Initiative from Field Evidence in Sri Lankan](https://aclanthology.org/2025.indonlp-1.13/) (Zhao et al., IndoNLP 2025)
ACL