Artificial Intelligence and Legal Research: Theory and Practice - Tatia Dolidze (Theoretical Hypotheses and Empirical Results, 2025)
Artificial Intelligence (AI) has significantly transformed legal research, impacting case law analysis, contract review, and legal analytics. With the ability to process vast amounts of legal data, AI enhances efficiency, reduces human error, and allows for predictive analytics in case law and regulatory compliance. This paper explores the theoretical foundations and practical applications of AI in the legal domain, emphasizing its advantages and challenges. The integration of AI into legal research has led to the development of sophisticated tools that support legal professionals in analyzing precedents, drafting legal documents, and predicting litigation outcomes. AI-driven legal databases and automated legal analytics streamline the traditionally time-consuming process of legal research, enabling professionals to focus on higher-level analytical work. However, concerns regarding bias in AI algorithms, transparency, ethical considerations, and regulatory compliance persist. This study investigates the methodologies and frameworks underpinning AI-driven legal research, providing a comparative analysis of existing AI legal research tools and their effectiveness. The paper also discusses the ethical and regulatory challenges surrounding AI adoption in legal studies and explores future trends in AI-driven legal research. By examining practical applications, this study aims to shed light on the opportunities and limitations AI presents in legal scholarship and practice, ultimately assessing its impact on the future of legal research.