Ranking Soil Quality Indicators Using the SMART Criteria, AHP Method and Chatbots

Soil is a complex environmental component characterized by numerous physical, chemical, and biological attributes that determine its capacity to provide ecosystem services. While composite soil health indices are traditionally derived from static literature reviews or costly expert panels, this study evaluates the potential of general-purpose large language models as a rapid, low-cost aid for AI-driven environmental decision support. We systematically evaluate the conceptual reliability and mathematical consistency of four distinct Large Language Models (LLMs): ChatGPT, Gemini, Claude, and Copilot, in executing an Analytic Hierarchy Process (AHP) matrix integrated with the SMART criteria. The evaluation prioritized seven indicators: soil organic matter (SOM), earthworm presence, aggregate stability, electrical conductivity, available nutrients, pH, and water infiltration capacity. All platforms generated mathematically consistent AHP matrices, with consistency ratios below accepted thresholds. Across ten independent runs per platform, soil organic matter received the highest mean global weight (~0.22) and earthworms the lowest (~0.08); inter-platform agreement was statistically significant (Kendall’s W = 0.75, p = 0.007), yet single-run variability reached ~50%, showing that repeated prompting is required. This approach offers a practical framework for rapid, preliminary indicator screening under resource-constrained conditions, while revealing model-specific divergences and possible training-data biases that warrant further investigation. However, our findings suggest AI-generated AHP outputs should be treated as preliminary decision-support results and should be verified through cross-platform comparison, repeated prompting, expert review, and site-specific validation.