Anayasa Mahkemesi Kararlarının Simülasyonu: Türk Bireysel Başvuruları İçin Çok Etmenli Bir Büyük Dil Modeli (LLM) Çerçevesi

This research examines the ability of large language models (LLMs) to emulate judicial decision-making in constitutional court cases. We used three cutting-edge models—GPT-5, Gemini, and Claude—to look at 343 decisions made by the Turkish Constitutional Court between 2014 and 2024. We did this using a two-stage evaluation framework that mirrored how courts really work. The study evaluated model efficacy in both admissibility determinations and substantive rights infringement judgments. During the admissibility stage (Stage 1), the accuracy rates varied from 68.80% for Claude to 81.34% for GPT-5, with majority voting achieving 79.59%. GPT-5 had the fewest total mistakes and a balanced approach, while Gemini and Claude were more likely to think that something was not admissible. On the other hand, courts had a more moderate acceptance rate. In Stage 2, when rights were violated, all three models had the same accuracy of 81.50%. However, majority voting did better, with an accuracy of 83.24%. In this case, GPT-5 tended to have partial matches, Gemini had the most exact matches, and Claude was in the middle, showing that each had different strengths in legal reasoning. Patterns of inter-model agreement showed that there was a lot of convergence, but it wasn't always the same. In Stage 1, unanimous agreements, though less common, had the highest accuracy (87.32%), showing that consensus decisions are reliable. In Stage 2, GPT-5 and Claude were the most in line with each other (88.52%). These results indicate that ensemble methods and hybrid human–AI approaches could improve the consistency and robustness of judicial decision-making. The results show that even general-purpose LLMs can understand complicated constitutional principles and come up with structured, court-like reasoning that is serious enough to be used in legal situations. Although existing models struggle to replicate the comprehensive intricacies of judicial reasoning, the persistent superiority of ensemble methodologies suggests that specialized legal AI systems may exceed general models, potentially revolutionizing constitutional jurisprudence by improving efficiency, consistency, and accessibility to justice