One of the world's top law schools draws a hard line against AI in legal education
UC Berkeley Law will ban AI from nearly all graded work starting in summer 2026, citing the need for students to learn critical thinking skills before leveraging AI.

In a bold move, UC Berkeley Law has announced plans to prohibit the use of artificial intelligence in nearly all graded work, effective summer 2026. This sweeping ban, which covers tasks such as outlining, drafting, and proofreading, is a significant departure from the growing trend of integrating AI into legal education. The only exception to this rule is the use of AI for research purposes.
The decision is rooted in the school's philosophy that future lawyers must first develop their critical thinking skills before they can effectively utilize AI tools. By doing so, students will be able to approach complex legal problems with a clear understanding of the underlying principles and theories, rather than relying solely on technology. "Future lawyers must first learn to think for themselves before they can use AI meaningfully," the school's administration emphasized.
This approach ensures that students are well-equipped to navigate the intricacies of the law and make informed decisions that are not solely dictated by algorithms. The ban on AI in graded work is a notable stance, especially considering the increasing prevalence of AI in various industries, including law. While some institutions have begun to incorporate AI into their curricula, UC Berkeley Law is taking a more cautious approach, prioritizing the development of essential skills over the adoption of emerging technologies.
As one of the world's top law schools, UC Berkeley's decision is likely to spark a renewed debate about the role of AI in legal education and the balance between technology and traditional skills. The school's commitment to fostering independent thought and critical thinking is clear, and it will be interesting to see how this approach shapes the next generation of lawyers. The article appeared first on The Decoder.
Source: The Decoder