Generative AI for Analyzing Participatory Rural Appraisal Data: An Exploratory Case Study in Gender Research

TL;DR

This paper investigates the use of Generative AI models to analyze unstructured visual data from participatory rural appraisal activities, highlighting current limitations and future needs in multilingual and culturally nuanced contexts.

Contribution

It provides an exploratory case study evaluating state-of-the-art LLMs' capabilities and challenges in interpreting participatory rural data for gender research.

Findings

AI models struggle with multilingual content and cultural nuances.

Models show promise in visual interpretation but face challenges in contextual accuracy.

Significant limitations in handling complex, unstructured participatory data.

Abstract

This study explores the novel application of Generative Artificial Intelligence (GenAI) in analyzing unstructured visual data generated through Participatory Rural Appraisal (PRA), specifically focusing on women's empowerment research in rural communities. Using the "Ideal Village" PRA activity as a case study, we evaluate three state-of-the-art Large Language Models (LLMs) - GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro - in their ability to interpret hand-drawn artifacts containing multilingual content from various Indian states. Through comparative analysis, we assess the models' performance across critical dimensions including visual interpretation, language translation, and data classification. Our findings reveal significant challenges in AI's current capabilities to process such unstructured data, particularly in handling multilingual content, maintaining contextual accuracy, and avoiding hallucinations. While the models showed promise in basic visual interpretation, they struggled with nuanced cultural contexts and consistent classification of empowerment-related elements. This study contributes to both AI and gender research by highlighting the potential and limitations of AI in analyzing participatory research data, while emphasizing the need for human oversight and improved contextual understanding. Our findings suggest future directions for developing more inclusive AI models that can better serve community-based participatory research, particularly in gender studies and rural development contexts.