Let’s face it. Most of our students now use AI for everything even if we all pretend otherwise—from summarizing dense readings to drafting emails, preparing for class, writing thesis proposals, entire essays, and yes, even PhD chapters. According to a 2024 study by the digital education council, 86% of students already use AI in their studies. While AI cannot yet consistently produce work that passes on its own, a literate user—one who understands prompting and the logic of large language models—can produce results that are passable, even polished, even at the graduate level. But students are not the only ones. Many academics now rely on AI tools as well—for course preparation, for generating PowerPoints, writing emails, responding to students, and increasingly, even for marking papers. In all these uses, AI has become an infrastructural support for academic life.
And yet, while there are clear ethical concerns tied to these practices, those concerns remain blurry, fragmented, and inconsistently articulated. For some, using AI is little more than the 21st-century equivalent of using spellcheck or Google; for others, it is a serious breach of academic integrity. Most people, though, fall somewhere in between—and the jury is still very much out to lunch. What’s striking is that for now, at least, the presence of these ethical questions does not appear to be deterring anyone.
This raises deeper and more uncomfortable questions. If our methods of assessment and our expectations about student preparation are out of sync with what’s actually happening in real academic and professional settings—and if the skills we’re cultivating are no longer tightly aligned with what’s valued in professional practice—how should legal education evolve? This is not a problem that can be answered in the abstract. Legal education is always situated, embedded within specific contexts—jurisdictional differences, access to technology, language proficiency, AI literacy, professional cultures, and the commercial environment all shape what is possible and what is desirable. A student in a Canadian law school and one in a rural university in Egypt will experience the AI transition very differently. Still, the core questions must be asked. What should academic legal education consist of today? What counts as academic knowledge in an age where information retrieval, summarization, and even basic drafting can be outsourced to machines? How will professional certification evolve when traditional benchmarks—writing a PhD, publishing papers, mastering citations—can be reached far more quickly with the right tools? Does it still make sense to ask someone to labor over a dissertation for three years if something that looks and feels credible can be produced in twelve months or less? What is the purpose of prioritizing publications, when a seasoned academic with prompting fluency can churn out a well-structured article in a weekend?
These are not rhetorical provocations. They are questions that demand rethinking—not just of what we teach, but of what we consider knowledge to be, and what we mean when we say someone is “qualified.”
1. What Counts as Legal Knowledge Now?
Legal knowledge has long been associated with a particular repertoire of skills: reading and interpreting texts, recalling precedent, articulating coherent arguments, and expressing these in grammatically and rhetorically competent prose. It has also been tightly linked to form—essays, case notes, doctrinal analysis, briefs, and theses—all of which have historically signified not just the student’s familiarity with legal content, but their capacity to produce this content in institutionally approved formats. However, the rise of AI in both legal education and legal practice invites us to re-examine what we mean by knowing the law. When generative tools can retrieve case law, draft arguments, summarize dense theory, and mimic citation styles with remarkable ease, the emphasis begins to shift from traditional forms of content production to something else—perhaps to evaluative judgment, critical selectivity, and most crucially, to a rearticulation of academic discernment.
Yet this is not an epistemic revolution. It is a reconfiguration—one in which long-standing forms of knowledge practice are revaluated, rather than replaced. Academic discernment has always been central to legal scholarship. The ability to tell the difference between a strong argument and a superficial one, to identify what matters in a body of case law, to recognize patterns across seemingly unrelated domains, or to spot conceptual slippages—these are not new skills. What is changing is their visibility and centrality. In an environment where machines can now do much of the assembling, retrieving, and even drafting, what remains human (at the moment) is the capacity to discern—to question the framing, to test the coherence, to notice the subtleties that fall through the algorithmic net, and, if anything, the desire to have humans perform this role.
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