AI-Enhanced TRIZ: Integrating 9 Windows Model with Large Language Models and Automatic Speech Recognition for Systemic Problem-Solving in Desertification Mitigation

Resumen

Engineering projects in desertification-affected regions like Valparaíso, Chile, must address complex environmental, technical, and socio-economic challenges such as water scarcity and soil degradation. Traditional Root Cause Analysis (RCA) methods often fall short in these dynamic contexts due to limited scalability and adaptability. This study presents a novel methodology integrating Artificial Intelligence (AI), Large Language Models (LLMs), and Automatic Speech Recognition (ASR) to enhance RCA in environmental adaptation and mitigation efforts. The approach leverages the 9 Windows Model from the TRIZ methodology for multi-level, time-scaled problem analysis. It involves three stages: (1) collecting and transcribing environmental discussions via ASR, (2) using LLMs to extract RCA insights aligned with the 9 Windows framework, and (3) generating automated reports with visualizations and strategic recommendations. A case study in Valparaíso examines the impact of desertification on water availability and agricultural productivity, demonstrating improved decision-making speed and quality. The approach reduces diagnostic time and supports more effective mitigation strategies. While AI-related challenges like bias and data dependency persist, the study emphasizes the importance of a human-in-the-loop model. This research offers a scalable, structured framework for applying AI to environmental management and supports innovation in multidisciplinary problem-solving.