The concept of legal intelligence is evolving far beyond simple legal research automation. Researchers are now defining “legal general intelligence” (Legal GI) as the capacity of artificial intelligence to perform with expert-level ability across complex legal contexts, including the interpretation of provisions, sound inference, conflict resolution between multiple legal domains, and making normatively binding judgments in ethically sensitive contexts. This represents a fundamental shift from task-oriented tools to systems that can participate in the normative structure of legal systems themselves. Benchmarks like LexGenius have emerged to systematically evaluate this capability, moving beyond outcome-focused assessments to examine the underlying reasoning processes.
The gap between AI and human legal professionals remains substantial. Recent evaluations of 12 state-of-the-art large language models on legal intelligence benchmarks reveal significant disparities across legal abilities, with even the best-performing models lagging behind human legal experts. The challenge is particularly acute in what researchers call “soft legal intelligence”—areas like ethical judgment, law-morality boundaries, and societal impact assessment. Traditional legal benchmarks have focused on technical tasks while overlooking these critical dimensions, creating a false impression of AI competence.
To address these limitations, frameworks are being developed to structure legal intelligence evaluation across multiple dimensions, tasks, and abilities. These frameworks draw on Bloom’s Taxonomy of Educational Objectives, covering the cognitive hierarchy from remembering and understanding to creating, alongside modular models used in legal evaluations across countries. The goal is not just to test whether AI knows the law, but whether it can engage with the full complexity of legal practice. As these evaluation frameworks mature, they pave the way for legal AI to move “towards the second half of AI,” where systems demonstrate genuine understanding rather than pattern mimicry.