Diminishing Practicality and Professionality from LL.B to a Theoretical Academic Programme

O.P.Jindal Global University

Astha, SEKSARIA, O.P.Jindal Global University; Ramkumaar, MADHAVAN, O.P.Jindal Global University ; Sanya, SINGH, O.P.Jindal Global University; Shauryavardhan, TOMAR, O.P.Jindal Global University.

Training / education / pedagogy

The AI use case we are addressing focuses on bridging the pedagogical gap between theoretical legal education and the practical application of AI tools. Historically, law students struggled to navigate traditional legal databases, learning them only through internships rather than in the classroom. We draw a parallel with AI: students already use AI extensively, but without formal training in prompt engineering. Our policy objective is twofold: first, equip students with prompt engineering skills to optimize their use of AI; second, shift professors’ mindsets so they embrace AI as an ally. We believe AI can enhance professors’ effectiveness without diminishing the human element, through AI copilots, feedback tools, and AI-assisted curriculum design. This ensures AI is integrated as a constructive tool, not a barrier, in the legal education ecosystem.

This use case matters because legal education, being a professional pathway, cannot be confined to traditional academic methods. In practice, law firms already leverage AI, many host internal AI systems to sift through vast document repositories and generate boilerplate clauses efficiently. In litigation, lawyers use AI to enhance their research and argumentation along with judges who use them to enhance their judgements. Thus, AI is already embedded in professional workflows. The gap is clear: while AI is a core tool in practice, law schools lag in systematically teaching it. Our goal is to align the higher education infrastructure with the profession’s realities. Students must learn AI as an asset just as they would core legal principles so that their practical capabilities match the profession they are entering.

At the core, our motivation is about making sure law students don’t face the gaps in practical application of legal principles. We’ve all been in situations where AI tools gave us a great start in research or analysis, but we had to figure out how to make that work on the job. We don’t want the budding lawyers to have to learn that by trial and error. We want AI to be a natural part of their legal education, not just something students stumble into, but something taught intentionally. And we want professors to see it as part of their toolkit, so everyone is better prepared for the profession.

Our initial contribution is a structured roadmap aimed at closing the gap between how law is taught in universities and how it is actually practiced in an increasingly AI-driven professional environment. We begin by recognising that interacting with AI, commonly referred to as prompt engineering, is a two-sided process. On one side is the user, who gives instructions or asks questions. On the other side is the AI, which interprets and responds. As future legal professionals, we do not have control over how AI systems are built or improved internally. That lies outside our scope. What we can influence is the user side, which means how individuals engage with AI to extract value from it. Within this user side, there are two primary actors. These are students and professors. Together, they shape how legal education is delivered and experienced. Our contribution focuses on strengthening both. For students, this means developing prompt engineering as a core skill. They must learn how to ask precise and structured questions so that AI outputs are useful and aligned with legal reasoning. For professors, this means integrating AI into teaching methods. AI should not replace traditional learning. It should complement it and reflect how law is practiced today. Our emphasis on prompt engineering is deliberate. It is not just a technical skill. It is a thinking skill. Many students struggle because they are not trained to frame the right questions. In law, the ability to ask the right question is critical. Prompt engineering trains this ability. At the same time, we recognise resistance among faculty. We identified what may be called consent fatigue. This is a reluctance to adopt new technology because of the effort it requires. To address this, we propose engaging with professors who support AI and those who are sceptical. This will help us understand the resistance and reduce the taboo around AI. This would delineate the critical differentiation between responsible AI use that is ethical, transparent, and bias-mitigated or its abuse through manipulation or unchecked deployment. We also propose real-time AI-assisted legal study. Law is dynamic. Amendments and new interpretations happen frequently. Traditional coursework often cannot keep up. AI allows students to stay updated in real time. This ensures that learning is not limited to static material. For example, students often begin jurisprudence with thinkers like Socrates or Aquinas. While important, modern scholars continue to reshape these ideas. AI can bring these contemporary developments into everyday learning. Our approach has been shaped by internal debates. Initially, we considered developing an AI teaching bot to assist students. This seemed efficient. However, we identified a risk. Such a system could allow students to rely on AI instead of thinking for themselves. This would weaken engagement with the material. We moved away from this idea. Instead, we focused on solutions that require active student participation. We also debated how AI should be used in assessment. At first, we considered a subjective feedback system led by professors. This would provide continuous and personalised evaluation. However, this approach risked embedding individual bias. It could create a narrow idea of what a correct answer is. This would limit creative legal thinking. We then shifted to an objective AI-driven quiz system. This method tests understanding and application in a structured way. It avoids imposing a single correct perspective. It allows multiple valid interpretations and encourages independent thinking while maintaining fairness. Taken together, our contribution is based on a clear principle. AI should strengthen engagement and thinking. It should align education with practice. It should not become a shortcut that replaces effort. By focusing on prompt engineering, real-time learning, and objective assessment, and by addressing both students and professors, we aim to build a legal education system that is both rigorous and relevant.