Utilizing ChatGPT as a Large Language Model for Qualitative Decision Tree Modeling: A Proof-of-Concept for Strengthening Food Security in Indonesia - Rachmadhan, Aditya Arief; Wijayati, Prasmita Dian; Widayanti, Annisa Vira; Hariputra, Akbar; Dewanti, Rizki Puspita (Jurnal Riset Multidisiplin Agrisosco, 2026)
This study explores the use of Artificial Intelligence, specifically ChatGPT as a large language model, in constructing a qualitative decision tree to support food security analysis in Indonesia. Framed within the FAO’s four-pillar approach—availability, access, utilization, and stability—the research adopts a methodological proof-of-concept design and does not rely on primary or secondary empirical datasets. Instead, the analysis is based on AI-generated reasoning derived from structured prompts, which are systematically organized into a conceptual decision tree framework. Validation is conducted through interpretative comparison with established theoretical frameworks and national policy documents, rather than empirical testing or expert elicitation. The resulting model provides a structured representation of strategic pathways and potential policy options, highlighting the advantages of AI-assisted modeling in terms of speed, scalability, and integrative synthesis of knowledge. However, the model remains qualitative and exploratory, with limitations related to contextual specificity, potential bias, and the absence of real-time data. The findings suggest that AI can function as a complementary analytical tool for structuring policy-relevant insights, although its application requires careful validation and should not be interpreted as evidence of policy effectiveness.