Key highlights:

• • • The theory-practice gap is now an AI-era crisis鈥� Integrating legal training with hands-on pro bono experience is the future of legal education.

    • A collaborative model merges learning and doing into a single platform鈥� The model connects law students with vetted pro bono opportunities from legal services organizations, while also offering targeted, skills-based training at the moment students step into those matters.

    • Pro bono work is uniquely suited for responsible AI training鈥� On-demand programs led by expert faculty are available to help students sharpen pro bono skills, understand the use of AI in today鈥檚 legal practice, and stay on top of developments in numerous industry and practice areas.

Legal education has operated on a familiar, decades-long divide that saw students spend their first years learning the law in the classroom and then after graduation, gaining substantive experience practicing the law in the real world. This gap has always been costly for both students and legal employers, and now it鈥檚 emerging as untenable in an era in which AI is rapidly reshaping what junior lawyers do.

Pro bono and skills training close this gap

A new partnership between , a pro bono management platform, and the (PLI), a nonprofit provider of learning resources for legal professionals, is designed to close this gap while showing something larger about where legal education must go.

The partnership is designed to equip students with on-demand, actionable training that supports effective pro bono engagement by offering access to PLI’s training programs directly through Paladin’s platform. Since launching with 30 law schools in August 2025, students have signed up for thousands of pro bono cases through the platform, according to , Co-founder and CEO of Paladin.

For years, experiential learning in law schools was something students had to piece together on their own by hunting across spreadsheets, clinic listings, and externship postings for opportunities, says Sonday, adding that too often students were given little guidance on what they were walking into.

The partnership is designed to equip students with on-demand, actionable training that supports effective pro bono engagement

“What’s fundamentally different is the integration and centralization of learning and doing,” Sonday explains. “Historically, legal education has separated theory, training, and practice.” Now, she notes, a student can learn a concept, build confidence through targeted training, and apply it in a real-world setting within a short amount of time.

, Chief Strategy Officer at PLI, describes the experience from the student’s perspective: 鈥淲hen a first-year logs into the Paladin platform, they are not thrown into the deep end. Instead, they can access skills-based programs, such as a PLI program specifically on how to interview a pro bono client before they ever sit across from someone in need. This leads to a better experience for the student, the law school, and especially for the client.”

Pro bono work suited to responsible AI training

The urgency behind this partnership is inseparable from the impact AI is having on the entry-level legal market.

“We’re already seeing AI reduce the time spent on tasks like initial legal research, document review, drafting memos, and summarizing case law,鈥� Sonday says. 鈥淭his is work that has traditionally formed the foundation of junior associate training.鈥� The skills AI cannot replicate 鈥� such as judgment, issue spotting in ambiguous situations, client communication, and ethical decision-making 鈥� are what students need to develop deliberately earlier in their legal careers.

Indeed, those human skills are essential to the effective use of AI, Talmage says. The lawyer of the future will be a strategic advisor and creative problem solver, which are the very attorney roles that AI cannot fill, she explains, adding that those must be cultivated through experience. “You always need to be questioning and verifying and authenticating 鈥� and that’s generally a lawyer鈥檚 role.鈥�

For years, experiential learning in law schools was something students had to piece together on their own by hunting across spreadsheets, clinic listings, and externship postings for opportunities.

There is a particular logic as to why pro bono work is the right fit for learning to use AI responsibly. Pro bono is “a built-in, humans-in-the-loop model” in which students are always supervised by attorneys, Sonday says. And this supervision creates a structured environment in which to learn how to use AI tools, apply them to real matters, get feedback, and iterate. The result, Sonday argues, will be more attorneys who are AI-fluent early on and throughout their careers.

A message to law school leaders

For law school leaders, both Sonday and Talmage highlight that AI use has already changed the legal profession. The choice then for law schools is whether they evolve by design or by default.

Students know the legal profession has changed and so do employers, CLE providers, and clients, Talmage explains.

Sonday agrees. “The pace of change in the legal profession is accelerating, and students need to be prepared not just for the law today, but also for the practice of law in the future,鈥� she says. 鈥淚ntegrating pro bono platforms and AI-specific training aligns legal education with reality.”

The Paladin/PLI partnership offers a blueprint for what legal education must become in the future, transforming itself into a space that鈥檚 grounded in applied legal knowledge, human-supervised, and AI-informed. Indeed, the best way to train the next generation of lawyers is to give them real clients, real cases, and real responsibility while they still have room to grow.

You can find more about the challenges facing law schools and legal education here

Key highlights:

• • • Curriculum听redesign must start now 鈥� One law school鈥檚 approach illustrates the necessity of mapping the entire curriculum to identify which skills to preserve, evolve, or build from scratch.

    • Training faculty in AI use is critical 鈥� Faculty AI training should be a multi-layered approach including hands-on training with specialized legal AI tools, guidance on redesigning curricula, and more.

    • AI simulations may be the key 鈥� Law school leaders need to act now by experimenting with small pilot projects and building simulation-based learning tools to replace the developmental depth that once came naturally in the first years of practice.

The debate about AI consuming most of the work that teaches essential lawyering skills to junior attorneys is forcing a reckoning with the long-held assumption that law schools were never designed to produce practice-ready lawyers and that it was always the profession’s job.

Indeed, AI is forcing that uncomfortable truth into the open faster than anyone anticipated because essential lawyering work 鈥� the document review, contract markup, research memo creation 鈥� dictated how a junior lawyer learned to spot the issue buried on page 47, to sense when a clause was off, and to develop the instinct that no classroom can fully replicate. Now, as more law firms deploy AI to handle precisely those entry-level tasks, the organic training moments that used to define the first two to three years of legal practice are evaporating.

, Executive Dean, Faculty of Law at Bond University, and Co-Chair of the Council of Australian Law Deans, says he sees where this is leading. The ultimate results will be firms hiring fewer junior lawyers today because AI has taken over that entry-level work, James explains, adding that means there will simply be no pipeline of mid-level, experienced lawyers to draw from in three to five years. Indeed, this is a slow-moving crisis, already in motion, and yet to fully arrive.

This crisis lands at the center of what the AI and Future of Legal Practice (AIFLP) initiative exists to address because at the core of this crisis is what does being job-ready really means when the job itself is being redefined. Answering this question requires law schools, law firms, licensing bodies, and technologists to do something they have historically struggled to do 鈥� that is to think and act collaboratively.

Rethinking the curriculum before AI does it for you

leads IE Law School鈥檚 AI initiative and is steering the school鈥檚 efforts to embed AI across the curriculum. To do so effectively, her approach requires going back to a broader set of foundational questions in legal education such as: For what is legal education meant to prepare students? How do students learn to develop legal judgment? What makes legal advice genuinely valuable? And what skills are essential to deliver that value in an AI-enabled profession?

鈥淟ayering AI tools on top of an unchanged curriculum serves no one,鈥� Perez-Llorca explains, adding that without answers to the fundamental questions, 鈥測ou are just adding technology to a structure that was never designed to handle it.鈥�

Check out how one law school professor is building AI simulation tools

IE law school is currently mapping its entire curriculum to determine which skills need to be preserved, which need to evolve, and which need to be built from scratch, while also using the AI-boosted curriculum to train faculty. Perez-Llorca describes the school鈥檚 faculty AI training as a multi-layered approach encompassing university-wide LLM training, substantive AI law curriculum review, hands-on training with specialized legal AI tools, guidance on redesigning curricula, and assessments to reflect students’ growing AI proficiency. Before students can be taught with AI, professors need to understand the tools themselves and how to use them in teaching, in simulation, and in assessment, she adds.

An AI tutor that meets students where they are

Bond University鈥檚 James says he has spent the last several months building an AI tutor designed to walk students through course material the way a patient, attentive instructor would. His vision for the AI teaching assistant supports the professor meeting students where they are. 鈥淚t [the AI tutor] introduces the week’s topic, outlines learning outcomes, guides students through the readings, checks comprehension with short quizzes, and then adapts in real time based on how the student responds,鈥� James explains, adding that the AI tutor will pull any student who is struggling deeper into the material until the learning outcome is achieved. 鈥淭he conversation never stops until the learning does.鈥�

However, James is careful to draw a clear distinction about what the tutor replaces and what it does not, stressing that AI is a substitute for the lecture recording, the static reading list, or the passive video watched at midnight before an exam 鈥� but it chiefly exists to support the law professor. This approach frees up class time, turning it from content delivery to more meaningful the time between the human instructor and students, he adds.

Act by design or default

The approaches by both Perez-Llorca and James point to a way to address the question of disappearing tasks that teach essential lawyering skills as well as shift the center of gravity in legal education toward ways to foster developmental skills and legal judgment. Indeed, inertia is not a strategy, and law school deans and associate deans can be at the forefront of this fight by taking decisive action, including:

• • • Experiment freely 鈥� Investigate with AI on your own by starting small with a pilot project.
    • Strategically assign where AI goes 鈥� Decide where AI belongs in the curriculum, such as in courses focused on legal research and drafting as they become commoditized by AI. Also, determine in which instances AI does not belong, such as counseling clients through ambiguity, navigating ethical complexity, and advocating persuasively. Make sure these all remain led by human lawyers.
    • Focus on skills 鈥� Map your law school鈥檚 curriculum by identifying which skills need to be preserved, which skills need to evolve, and which need to be built from scratch.
    • Build AI-assisted teaching tools 鈥� Make experiential and simulation-based learning central to the curriculum.

鈥淭he choice is between dealing with this crisis by design or by default,鈥� James says, noting that the pipeline problem he described is already in motion while the practitioners, educators, technologists, and licensing bodies that need to solve this together are not yet consistently in the same room.

Watch our recent Clarity podcast to see

Key findings:

• • • Law students understand risks and opportunities of AI use 鈥� Almost three-quarters (72%) of students surveyed say they see AI literacy as essential, while an even larger portion (74%) say they also recognize the risks of over-reliance.

    • Student AI adoption is already widespread 鈥� Almost 6 in 10 law students use AI several times per week for academic work, but much of this learning is happening through self-education rather than structured teaching.

    • AI guidance in law schools remains inconsistent 鈥� Close to a majority (48%) of students report that AI policies vary by professor, and almost one-third (32%) say that their schools do not give them the AI skills needed for their future career.

There is a significant and growing divide between how law students understand artificial intelligence and how legal institutions, such as law schools, are responding to it, according to a new 成人VR视频 Institute white paper.

Jump to 鈫�

2026 Law Student Pulse Survey

The 2026 Law Student Pulse Survey, based on responses from more than 1,800 law students that were collected in April 2026, challenges two assumptions that have long dominated institutional thinking. The first is that students are reckless adopters who use AI to bypass the hard cognitive work of legal education. The second is that students are passive and uninformed consumers of a technology they do not fully grasp. The data shows that neither characterization is accurate.

In reality, 72% of responding students identify AI literacy as an essential professional skill, while 74% simultaneously acknowledge that over-reliance on AI could undermine the development of their own core legal competencies. Holding both of these positions in tandem reflects a level of professional maturity that many institutions have yet to demonstrate in their own policies and curricula.

The survey also exposes a serious institutional gap. Nearly one-third of students report that their school does not provide the AI skills needed for their future legal careers. And nearly half indicate that AI policies vary by professor, leaving students without coherent and consistent institutional guidance on what responsible AI use actually looks like.

Far-reaching consequences

The consequences of this AI-understanding gap extend well beyond the classroom. Students are entering the workforce self-taught and inconsistently prepared, at a moment when legal employers are moving quickly to embed AI fluency into their hiring and development expectations. The profession is at risk of producing graduates who are sophisticated enough to recognize the stakes but underprepared to meet them.

The full white paper outlines specific, actionable recommendations for law schools, bar associations and accreditors, and legal employers to follow to better address this gap in AI understanding.

You can download

a full copy of the 成人VR视频 Institute’s “2026 Law Student Pulse Survey” by filling out the form below:

Key insights:

• • • Lawyers need two types of judgment 鈥� AI is exposing gaps in legal judgment and business judgment, both of which attorneys need to differentiate their value as automation increases.

    • Legal and business judgment are not the same skill 鈥� Legal judgment produces lawyers who reason well about the law; business judgment produces lawyers who can translate that reasoning into something a business partner can understand and act upon.

    • Business judgment is essential in the AI era 鈥� Business judgment is the translation layer between legal analysis and business action, and it has emerged as a key part of the value proposition for lawyers in an AI-powered profession.

Every conversation about AI and its impact on how lawyers will learn judgment that is happening right now assumes the profession knows what judgment is. Yet, we鈥檝e spoken to two practitioners who demonstrate how differently they interpret what judgment is: One is talking about the ability to reason like a lawyer; and the other is talking about the ability to act like a business partner.

Both of these interpretations matter, and both are in the spotlight because of AI. Yet, the legal profession’s near-total focus on legal judgment, while remaining almost entirely blind to business judgment, may be a consequential mistake.

Significant discussion about legal judgment

The question about how to teach legal judgment in the age of AI within legal education is urgent and well-founded. For decades, junior lawyers have learned by doing, with legal instincts accumulated through repetition and proximity to experience.

鈥淭he whole model that corporate clients would subsidize the learning of junior lawyers is all going away [because of AI],鈥� says , founder of Creative Lawyers, a consulting and advisory service dedicated to transforming the future of legal practices. 鈥淐orporate clients already hated it, and now they have a way to say, 鈥業’m absolutely not paying for this.鈥欌��

The research, drafting, and document review tasks that once served as the informal training ground for legal judgment are those that AI is absorbing the fastest. The profession is right to sound the alarm. AI-powered simulation and knowledge tools are emerging as credible responses, and Leonard herself sees genuine promise in them. Now, firms can use decades of document management data to create AI-powered coaching environments, pattern-matching a partner’s stylistic preferences so associates can calibrate their work before it lands on a senior lawyer’s desk, she explains, adding that, unfortunately, inertia and the industry鈥檚 resistance to change have emerged as structural obstacles to this advancement.

Development of business judgment is lacking

, CEO at TermScout, a general counsel and product builder of legal and decision systems who has spent years developing tools for legal and business teams, looks at judgment from a completely different place, framing the issue as a practice problem instead of an education one.

The legal profession’s near-total focus on legal judgment, while remaining almost entirely blind to business judgment, may be a consequential mistake.

“Judgment isn’t one skill,鈥� Mack states. 鈥淚t’s a set of small decisions happening quickly: prioritization of what matters, articulation of trade-offs, mapping consequences, and translating all of that into something a business partner can act on.鈥� Her description of judgment is executive decision-making that happens to operate inside a legal constraint. More specifically, she refers to it as the translation layer between legal analysis and business action, or decision-making under constraint. 鈥淚f that translation doesn’t happen, the legal work doesn’t have much effect,鈥� she adds.

Comparing these two viewpoints side by side, legal judgment is focused on producing lawyers who reason well about the law; business judgment goes one step further by describing lawyers who reason well and who can translate that reasoning into something a business can act on.

AI has shined a spotlight on both judgment gaps even as it showcases the value of the AI-enabled lawyer. AI may give you answers, but judgment is deciding which answers matter and what to do. And at a time in which AI can deliver output with some legal reasoning faster, cheaper, and at greater scale than any junior associate, the translation layer is no longer a complement to a lawyer’s value proposition. Thus, that value proposition has to be addressed in an AI-enabled profession.

Why both views need to be addressed

The two judgment problems are equally urgent on the same timeline. New lawyers entering practice right now are expected to be AI-enabled immediately, and if they arrive with only legal reasoning capability and no translation layer, they will be outcompeted by the lawyers who have both legal and business judgment.

The good news is that legal judgment is already taught, but it is not taught evenly. The key question at play is whether the profession is willing to make teaching such judgment more explicit and consistent. Business judgment, like legal judgment, has always been distributed unevenly with the proper understanding of it going to those with the best mentors, the most consequential early experiences, and the greatest proximity to senior decision-makers. Explicit teaching of judgment frameworks, through deliberate simulations could level that playing field in ways the osmosis model never could.

The profession has one word 鈥� judgment 鈥� to teach as two different cognitive capabilities. Closing the gaps on both types requires the profession to stop treating them both as a natural byproduct of legal experience and start treating it as a foundational competency that must be taught deliberately, early, and at scale.

鈥淲hat humans bring to the partnership with AI is judgment,鈥� Mack says, demonstrating the kind of clarity that tends to arrive only after years of building things that work. 鈥淭his is not optional 鈥� it is mission critical.”

You can learn more about听the challenges facing legal talent here

Key insights:

• • • AI’s greatest strength in criminal justice is pattern recognition鈥� AI can process vast amounts of data quickly, helping law enforcement and legal professionals detect connections, reduce oversight gaps, and improve consistency across investigations and casework.

    • AI should strengthen justice, not substitute for human judgment鈥� Legal professionals are integral to evaluating AI-generated outputs, especially when decisions affect evidence, warrants, and individuals鈥� constitutional rights.

    • The most effective model is human/AI collaboration鈥� AI handles scale and speed, while judges, attorneys, and investigators provide context, accountability, and ethical reasoning needed to protect due process.

The law has always been about patterns 鈥� patterns of behavior, patterns of evidence, and patterns of justice. Now, courts and law enforcement can leverage a tool powerful enough to see those patterns at a scale at a speed no human mind could match: AI.

At its core, AI works by recognizing patterns. Rather than simply matching keywords, it learns from large amounts of existing text to understand meaning and context and uses that learning to make predictions about what comes next. In the context of law enforcement, that capability is nothing short of transformative.

These themes were front and center in a recent webinar, , from the听, a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI). The webinar brought together voices from across the justice system, and what emerged was a clear and consistent message: AI is a powerful ally in the pursuit of justice, but only when paired with the judgment, accountability, and constitutional grounding that human professionals can provide.

AI’s pattern recognition is a gamechanger

“AI is excellent,鈥� said Mark Cheatham, Chief of Police in Acworth, Georgia, during the webinar. 鈥淚t is better than anyone else in your office at recognizing patterns. No doubt about it. It is the smartest, most capable employee that you have.”

That kind of capability, applied to the demands of modern policing, investigation, and prosecution, is a genuine gamechanger. However, the promise of AI extends far beyond the patrol car or the precinct. Indeed, it cascades through the entire arc of justice 鈥� from the moment a crime is detected all the way through prosecution and adjudication.

Each step in that chain represents not just an operational and efficiency upgrade, but an opportunity to make the system more fair, more consistent, and more protective of the rights of everyone involved.

Webinar participants considered the practical implications. For example, AI can identify and mitigate human error in decision-making, promoting greater consistency and fairness in outcomes across cases. And by automating labor-intensive tasks such as reviewing body camera footage, AI frees prosecutors and defense attorneys to focus on other aspects of their work that demand professional judgment and legal expertise.

In legal education, the potential of AI is similarly recognized. Hon. Eric DuBois of the 9th Judicial Circuit Court in Florida emphasizes its role as a tool rather than a substitute. “I encourage the law students to use AI as a starting point,鈥� Judge DuBois explained. 鈥淏ut it’s not going to replace us. You’ve got to put the work in, you’ve got to put the effort in.”

AI can never replace the detective, the prosecutor, the judge, or the defense attorney; however, it can work alongside them, handling the volume and velocity of data that no human team could process alone.

Judge DuBois’ perspective aligns with broader judicial sentiment on the responsible integration of AI. In fact, one consistent theme across the webinar was the necessity of maintaining human oversight. The role of the legal professional remains central, participants stressed, because that ensures accuracy, accountability, and ethical judgment. The appropriate placement of human expertise within AI-assisted processes is essential to ensuring a fair and effective legal system.

That balance between leveraging AI and preserving human judgment is not just good practice, rather it鈥檚 a cornerstone of justice. While Chief Cheatham praises AI’s pattern recognition, he also cautions that it “will call in sick, frequently and unexpectedly.” In other words, AI is a powerful but imperfect tool, and those professionals who rely on it must always be prepared to intervene in those situations in which AI falls short. Moreover, the technology is improving extremely rapidly, and the models we are using today will likely be the worst models we ever use.

Naturally, that readiness is especially critical when individuals鈥� rights are on the line. 鈥淎 human cannot just rely on that machine,鈥� said Joyce King, Deputy State’s Attorney for Frederick County in Maryland. 鈥淵ou need a warrant to open that cyber tip separately, to get human eyes on that for confirmation, that we cannot rely on the machine.” Clearly, as the webinar explained, AI does not replace constitutional obligations; rather, it operates within them, and the professionals who use AI are still the guardians of due process.

The human/AI partnership is where justice is served

Bob Rhodes, Chief Technology Officer for 成人VR视频 Special Services (TRSS) echoed that sentiment with a principle that cuts across every application of AI in the justice system. “The number one thing鈥� is a human should always be in the loop to verify what the systems are giving them,” Rhodes said.

This is not a limitation of AI; instead, it鈥檚 the design of a system that works. AI identifies the patterns, and trained, experienced professionals evaluate them, act on them, and are accountable for them.

That partnership is where the real opportunity lives. AI can never replace the detective, the prosecutor, the judge, or the defense attorney. However, it can work alongside them, handling the volume and velocity of data that no human team could process alone. So that means the humans in the room can focus on what they do best: applying judgment, upholding the law, and protecting an individual鈥檚 rights.

For judicial and law enforcement professionals, this is the moment to lean in. The patterns are there, the technology to read them is here, and the opportunity to use both in service of rights 鈥� not against them 鈥� has never been greater.

You can find out more about the webinars from the AI Policy Consortium here

Key takeaways:

• • • Anchoring distorts judgment before you begin 鈥� Research shows a first draft shapes subsequent decisions; and an AI draft is the most seductive anchor imaginable, because it looks exactly like something a lawyer would write.

    • The First Draft Trap inverts legal training 鈥� The Socratic method builds the habit of holding multiple possibilities in tension before committing; but an AI first draft collapses that space before the real thinking begins.

    • The fix is to ask for the map, not the draft 鈥� Requesting multiple strategic framings before writing keeps judgment where it belongs and uses AI to expand possibilities rather than foreclose them.

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Welcome back to听The AI Law Professor. Last month, I examined why promised efficiency gains often become a cycle of work intensification. This month, I want to address a subtler challenge. I call it the First Draft Trap and understanding it may change how you reach for AI the next time a new matter lands on your desk

We have all heard the pitch: Staring at a blank page? Just prompt the AI. In seconds you have a working draft: structured, coherent, and surprisingly competent. The blank page problem, that ancient enemy of productivity, thus has been vanquished.

Except the blank page itself was never just an obstacle; rather, it was a space of possibility. For lawyers, it was the space in which the most important part of their work actually happens. Now, with AI in the mix, that may be changing.

Welcome to the First Draft Trap.

Simply put, the First Draft Trap is this: The moment you accept an AI-generated draft as your starting point, you have already made the most consequential decision of the entire project 鈥� most importantly, you made it by not making it. You let the machine choose your direction, your framing, and your theory. Everything that follows is editing; and editing, no matter how rigorous, is not the same as thinking.

The cognitive hijack

There is solid psychology behind why this happens. Daniel Kahneman and Amos Tversky demonstrated in their landmark 1974 paper, , that once people are exposed to an idea, this first impression distorts their subsequent judgments and becomes a mental anchor. In their experiments, subjects who watched a roulette wheel spin to a random number still let that number influence their estimates of completely unrelated quantities. The anchor held even when people knew it was meaningless.

Please join Tom Martin at the on April 28鈥�29. It鈥檚 virtual and completely free 鈥� two days of keynotes, panels, and workshops on AI and the legal profession

An AI first draft is the most seductive anchor imaginable. It is not random 鈥� it is plausible, and it is well-organized. It sounds like something a lawyer would write. And that is precisely what makes it dangerous. You know intellectually that it is just one of many possible approaches to addressing the matter, but the anchor holds anyway.

That is the First Draft Trap at the cognitive level. The AI draft is not just one option you happen to prefer. It is a filter that prevents you from seeing the other options that were available to you, the roads you never even noticed that you did not take.

Consider what this means for a profession built on the opposite instinct. From the first day of law school, lawyers are trained to resist the obvious answer and to think like a lawyer. The Socratic method exists for exactly this reason. A good professor hears your confident response and asks: What else? What if the facts were different? What is the argument on the other side? The goal is not to arrive at an answer, per se. It is to build the mental habit of holding multiple possibilities in tension before committing to any one of them.

The First Draft Trap is the anti-Socratic method. It delivers a confident answer before you have even formulated the question properly 鈥� and instead of interrogating it, you polish it.

The value of the blank page

Think about what a senior partner actually does when a junior associate brings them a memo. The partner鈥檚 value is not better writing; rather, it is peripheral vision: The ability to see what the memo does not address, the argument not considered, or the framing that would land differently with this particular judge or this particular jury. That capacity to see beyond the document in front of them is why clients pay senior partners premium rates. And it is precisely the muscle that atrophies when your default workflow begins with the prompt generate a draft.

The AI draft is not just one option you happen to prefer. It is a filter that prevents you from seeing the other options that were available to you, the roads you never even noticed that you did not take.

The two-system framework offered by Kahneman and Tversky gives us a clean way to describe what is going wrong. System 1 is fast, intuitive, and pattern-matching; while System 2 is slow, deliberate, and analytical. The practice of law, at its best, is a System 2 discipline. We, as lawyers, are trained to override gut reactions, challenge assumptions, and think through consequences before acting.

In this way, the AI first draft feels like a System 2 output. It is structured, footnoted, and methodical. However, your decision to accept it as a starting point is pure System 1 鈥� a fast, intuitive grab at the nearest plausible answer. You have used a sophisticated tool to bypass the sophisticated thinking the tool was supposed to support. That uncomfortable period of ambiguity, of not knowing which path is best, is where the real lawyering lives.

What to do instead

None of this means stop using AI. It means stop using AI to skip the hard part that matters.

Before you ever ask for a draft, ask for the map. Describe the matter or document you are working on, then ask the AI for three fundamentally different strategic framings for the problem. For each framing, request the strongest argument in its favor and its most serious vulnerability. Then ask which framing best fits the client鈥檚 goals, the audience, or the procedural posture. Close with a clear instruction: Do not write a draft yet.

That last instruction is the key. It keeps you in the driver鈥檚 seat during the phase that matters most. You are using AI to expand the possibilities before you prune them, not after. And, most importantly, it gives you the opportunity to think for yourself about other important possibilities and add them in.

In the terms used by Kahneman and Tversky, use AI to fuel System 2, not to hand the controls to System 1. Let the machine generate options, and you exercise judgment.

For lawyers, the ability to see what is not there is the whole game.

Do not let the first draft blind you to it.

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and author of the forthcoming听. He is 鈥淭he AI Law Professor鈥� and writes this eponymous column for the 成人VR视频 Institute.

Key takeaways:

• • • AI threatens traditional lawyer development 鈥� As AI automates entry-level legal tasks like research and writing that historically has honed legal judgment skills, the profession faces a crisis in how new lawyers will develop such judgment abilities.

    • The profession can鈥檛 agree on what constitutes “legal judgment鈥� 鈥� Unlike other professions, there is no agreed-upon definition of legal judgment or clear standards for when AI should be used.

    • Implementation requires unprecedented coordination and funding 鈥� A legal education fund as a proposed solution would require a small percentage of legal services revenue and coordinated action across law schools, legal employers, and state regulators.

This is the second of a two-part blog series that looks at how lawyer training needs to evolve in the age of AI. The first part of this series looked at how lawyers can keep their skills relevant amid AI utilization.

The key skills that comprise legal judgment have received mixed reviews, according to a recent white paper from the 成人VR视频 Institute that advocated for cultivating practice-ready lawyers. The white paper was based on feedback from thousands of experienced lawyers, judges, and law students and raises questions about how legal judgment forms when AI assistance is used for task completion.

notes that calls for 鈥�鈥� to accelerate the development of legal judgment early in lawyers鈥� careers.鈥�

The challenge is that each part of the profession 鈥� law schools, employers, state supreme courts (as regulators) 鈥� have distinctly separate responsibilities. That means, that in the age of AI, coordination across the entire legal profession is needed, especially as AI reduces the availability of traditional first jobs.

Furlong points out that there is no consensus for what legal judgment is or any agreed upon standards for in what instances AI should be used in legal. To bring clarity to these issues, the white paper proposed a profession-wide model that integrates three critical elements: i) work-based learning that鈥檚 modeled on medical residencies; ii) micro-skill decomposition of legal judgment; and iii) AI-as-thinking-partner throughout pedagogy.

Three pillars for an AI-era lawyer formation system

Not surprisingly, overreliance on AI can erode critical analysis and solid legal judgment skills. Addressing these concerns requires a comprehensive reimagining of how lawyers are educated and trained. One solution lies in three interconnected pillars that together form a cohesive system for developing legal judgment in an AI-integrated world.

Pillar 1: Integrate work experience into legal education

Core skills such as legal research, writing, and document review help develop legal judgment; yet these skills could collapse once AI assumes such tasks. The Brookings Institution recently proposed to preserve entry-level professional development in an AI era. This parallels the TRI white paper鈥檚 calls for mandatory supervised postgraduate practice as a key part of legal licensure.

While implementing a full residency model presents challenges, several law schools have already pioneered approaches that demonstrate the viability of work-integrated legal education that, if scaled appropriately, could improve new lawyer practice and judgment skills. For example, Northeastern Law School guarantees all students nearly before graduation through four quarter-length legal positions. The program integrates supervised practice into the curriculum so graduates can gain substantial hands-on experience alongside their classroom instruction.

Also, program offers an alternative pathway to bar admission through practice-based assessment rather than the traditional bar exam. The program demonstrates that competency can be evaluated through supervised experiential learning.

Pillar 2: Decompose legal judgment into teachable micro-skills

The legal profession needs to come to a common definition of legal judgment and develop its components to teach the concept effectively. “We can’t teach what we can’t describe,” Furlong says. To develop legal judgment, the profession must define its components, including:

• • • Pattern recognition 鈥� The ability to identify when different fact patterns are related to similar legal frameworks and distinguish when superficially similar cases are legally distinct.
    • Strategic calibration and proportionality 鈥� This means understanding what level of effort, precision, and risk each matter requires and matching responses to the stakes involved.
    • Reasoning through uncertainty 鈥� This is the capacity to make defensible decisions and provide sound counsel even when the law is ambiguous, unsettled, or silent on an issue.
    • Source evaluation and authority weighting 鈥� This includes knowing which legal authorities are most suitable and being able to assess their persuasive value.
    • Ethical judgment under pressure 鈥� This means spotting conflicts, confidentiality issues, and duty-of-candor moments while maintaining competence and knowing when to escalate beyond expertise.

Breaking down legal judgment into these discrete components makes it possible to design targeted teaching interventions. For example, , former law professor and executive director of , suggests we back into AI-assisted workflows by requiring a short verification log (detailing sources checked, changes made, and why); running attack-the-draft drills (find missing authority, weak inferences, and jurisdictional mismatch); and preserving slow work as formative work (citation chaining, updating, and adversarial research memos).

With judgment skills clearly defined and work experience integrated into training, the profession must then tackle how AI itself should be incorporated into lawyer development.

Pillar 3: AI-as-thinking-partner throughout a lawyer鈥檚 career

Warnings that are mounting. The legal profession must provide clear standards for in what instances and how AI should be used, with training in verification and judgment skills. Overreliance on AI could compromise lawyers’ capacity to fulfill their fiduciary duties to clients.

A phased approach in the introduction of AI in legal work helps protect critical thinking while building AI competency. For example, in Year 1, law students could complete core legal reasoning exercises without AI assistance in order to better develop their analytical muscles. In Year 2, students use AI as a research assistant with mandatory verification protocols that teach students to check outputs against authoritative sources. Finally, in Year 3, residencies can immerse students in real-world AI workflows under proper supervision and while providing feedback.

These three pillars form a coherent vision for lawyer formation in the AI era. However, the most well-designed system faces the obstacle of funding.

The challenge of who pays

Perhaps the most difficult part of any overhaul is the cost. The medical residency model works because 鈥� up to $15 billion-plus annually 鈥� for teaching young medical students to be doctors. Legal education has no equivalent. Without addressing funding, however, even the best reforms will fail.

One idea is to establish a legal education fund that鈥檚 supported by an assessment of a small percentage of the legal industry鈥檚 gross legal services revenue (while exempting solo practitioners and firms with less than $500,000 in annual revenue). These funds could be used to subsidize thousands of supervised residency placements, fund law school curriculum development, support bar exam alternative assessments, and provide employer training and supervision stipends.

The challenge is that each part of the profession 鈥� law schools, employers, state supreme courts 鈥� have distinctly separate responsibilities, and that means coordination across the entire legal profession is needed.

This proposal, of course, would require unprecedented coordination and financial commitment from the legal profession. Skeptics might argue that market forces can solve this problem, or that firms will simply create new training pathways, or that AI will prove less disruptive than feared. However, waiting for market forces risks a lost generation of lawyers. The medical profession already when the medical industry鈥檚 voluntary reform failed. Only later did coordinated regulatory intervention produce the consistent quality standards the medical industry sees now.

What is clear is that inaction is resulting in degradation of lawyering skills. 鈥淢aybe… we need catastrophic external intervention to bring about the wholesale changes we can’t manage from the inside,” Furlong suggests.

However, the question is whether the legal profession will wait for a crisis to force change or act proactively to make the needed changes now, before the crisis hits.

You can learn more about the impact of AI on professional services organizations at TRI鈥檚 upcoming 2026 Future of AI & Technology Forum here

Key insights:

• • • • Creating prototypes of IP-protected teaching tools 鈥� Law school faculty can build working AI teaching tool prototypes in one to two hours without IP worries because key optional settings enable a closed system to ensure professors’ intellectual property remains protected.

      • Strong prompting skills create faster prototypes 鈥� The best instructions initially set the AI’s character, explains what the AI needs to accomplish, lists which documents to reference exclusively, describes how the response should be formatted, and mentions any applicable legal jurisdiction limits.

      • Feedback from students is positive 鈥� Students鈥� responses show AI simulators reduce anxiety and build confidence by providing unlimited low-stakes practice opportunities that make legal concepts more digestible through active dialogue rather than passive reading.

Law schools face a persistent challenge on how to provide individualized skills practice when one professor must serve many students. And today鈥檚 traditional legal education offers limited opportunities for students to practice oral arguments, evidentiary objections, and witness examinations. Indeed, the repetition necessary to build authentic courtroom skills does not scale easily with law professors in the classroom alone.

To address this challenge, at the University of Missouri鈥揔ansas City School of Law听 that simulate trial judges, three-panel appellate courts, witnesses, and evidentiary objection scenarios. Prof. Serra has seen firsthand how these tools give students unlimited, low-stakes practice opportunities that reduce their anxiety while building confidence in their legal reasoning and judgement.

Building your first AI learning tool, step by step

Creating custom AI teaching tools requires far less technical expertise than most professors would assume. As Prof. Serra explains, if you have a general idea of what you want the tool to accomplish, then 鈥測ou can have a working prototype in less than two hours from idea to execution.”

The process begins with choosing a large language model (LLM) platform, such as ChatGPT, Claude, or Gemini, and securing a paid subscription, which most law schools will provide, she explains. During the sign-up process, optional settings enable a closed system to ensure law professors鈥� intellectual property is not shown to the students and is not used to train the LLMs.

Next, you should gather class materials, including slides, case files, manuals, and problems the professor has already created. After that, it is necessary to define one specific use case, such as an evidentiary objections practice tool, a Socratic method simulator, or a client interview assistant.

The building process itself takes about one to two hours and requires no coding skills. 鈥淵ou just start talking to the LLM like you are training a teaching assistant to do exactly what you want to do,” Prof. Serra adds.

Having built many tools, she highlights three critical components that are necessary for the efficient, useful, and flexible prototype. These include:

1. Prompting skills

Effective prompting is key to generating a good prototype. 听According to Prof. Serra, the ideal prompt includes defining the AI’s role (You are a trial judge in a federal district court), specifying the task the AI should deliver, identifying which documents to use exclusively, describing the desired output format, and including any jurisdictional constraints.

2. Multimodal features in AI tools

Most platforms allow for voice-activated chat mode, in addition to typing back and forth, which helps students respond out loud in real time. Custom AI tools also have shareable links, which enables easy deployment to students. Once a student engages with the tool, they can send back a transcript of the interaction. Some platforms even allow shareable audio files so students can get feedback from their professors on skills performance, not just content.

3. Verifying reliability

Evaluating the quality of the AI output is important but naturally varies by use case. For classroom tools, Prof. Serra recommends deploying prototypes quickly and using students as testers. If the tool produces outputs with inaccuracies, she encourages students to bring these errors to class for discussion. That way, everyone learns how to critically diagnose problems with AI outputs. A variety of problems cause AI inaccuracies 鈥� the AI itself, poor prompting, incorrect legal reasoning, or incomplete training.

For wider deployment without the builder鈥檚 direct oversight, Prof. Serra recommends an extended period of testing and iteration. Her tool, MootMentorAI, which simulates a three-judge appellate panel for first-year law students preparing for oral argument, is one example. Because MootMentorAI was developed for use by a colleague, Prof. Serra worked with a research assistant to conduct 80 simulations over the course of a semester 鈥� 40 from the plaintiff鈥檚 perspective and 40 from the defendant鈥檚 perspective 鈥� to verify reliability and improve performance before deployment without her supervision.

Overcoming adoption barriers among peers

Faculty resistance remains the most significant barrier to deploying AI-enabled teaching tools in legal education. “There’s lots of faculty pushback, distrust, and a healthy dose of skepticism with AI,” Prof. Serra acknowledges, arguing that even so, AI-powered tools are teaching assets for all law school courses. 鈥淓ven in doctrinal classes that run on traditional Socratic dialogue, professors can still use AI to reinforce learning outside the classroom through tools, such as podcast-style lectures, a multiple-choice practice assistant, tools to enable issue-spotting, and essay practice tied to course fact patterns.鈥�

Common concerns among law school faculty include confidentiality, intellectual property protection, fear of revealing exam content, and perceived lack of technical expertise. However, Prof. Serra points out that these fears often stem from her colleagues鈥� misunderstanding of how closed systems work. Indeed, if privacy settings are correctly deployed, uploaded materials will not be used to train public models and students cannot access source documents.

Indeed, the most effective strategy for overcoming resistance is personal demonstration, she says, noting that she frequently sits down with colleagues virtually to build tools based on the colleague鈥檚 own use case. She鈥檚 built everything from a Startup CEO simulator for a business course, to an interview assistant for Career Services, to a simulated forensics expert for students to cross-examine. This grassroots approach, combined with speaking at conferences and identifying super fans who can champion the technology, gradually builds institutional buy-in, she adds.

Multifaceted student feedback

Student feedback has been overwhelmingly positive, with learners describing how AI simulators make legal skills training more accessible, more engaging, and less intimidating. In fact, students are often surprised by how convincingly AI tools can simulate judges, witnesses, and other real-world lawyering scenarios. They also appreciate having permission to use AI as a legitimate learning aid.

They also report that real-time interaction makes course concepts more digestible because these tools turn learning into an active dialogue rather than passively staring at a casebook. Finally, students say the simulators reduce anxiety before oral arguments or presentations by enabling unlimited, low-stakes repetition that builds confidence and keeps practice from feeling overwhelming.

Clearly, AI tools are quickly becoming essential learning infrastructure, and legal education cannot afford to treat them as optional add-ons if it expects to stay relevant. As a growing chorus of educators and employers warns that institutions must evolve, the real question is whether schools will build responsible, faculty-guided systems fast enough to meet students where the profession is headed.

When deployed thoughtfully, these platforms can scale individualized skills training, deepen engagement beyond the casebook, and build durable confidence that law students can carry into their future legal practice.

You can download a full copy of the 成人VR视频 Institute鈥檚听recent white paper, , here

Key takeaways:

• • • There is no true agentic AI鈥� yet 鈥� We don鈥檛 have true agents yet, but the release of GPT-5 and the speed of improvements signal that agents will become ever more capable quickly.

    • There are 4 core principles of deployment 鈥� Deploying true AI agents in law and other high-stakes fields demands adherence to four core principles: transparency, autonomy, reliability, and visibility.

    • Deliberate design and balance needed 鈥� The future of AI agents depends on deliberate design choices that balance machine autonomy with human oversight, ensuring trustworthy and effective collaboration.

Welcome back for . Last month we took a 30,000-foot view of AI evolution and its five stages of development. This month, I鈥檇 like to take a closer view of AI agents and some principles we should be applying to their use. Let鈥檚 start by talking about what true AI agents are and what they mean for the practice of law.

Imagine this 鈥� a major law firm discovers their AI agent had been conducting legal research for three months despite a critical flaw: It was systematically ignoring case law from certain jurisdictions due to a visibility parameter no one knew existed. The AI agent had drafted hundreds of briefs, all technically accurate within its limited scope, yet all potentially catastrophic if filed. The firm caught it by accident, when a junior associate noticed a glaring omission that the AI had consistently made.

This near-miss isn’t an isolated incident. Across industries, we’re beginning to deploy AI agents to autonomously act in high-stakes environments, such as reviewing contracts, making medical recommendations, managing financial portfolios, even driving cars. We celebrate their efficiency and scale while harboring a gnawing uncertainty: Do we really understand what these systems are doing? Can we trust them when we can’t fully see how they see the world?

What is an AI agent?

Before diving into principles, let’s clarify what we mean by AI agent. The term gets thrown around loosely, often confused with agentic workflows, but there’s a crucial distinction.

An agentic workflow is a semi-automated process in which AI assists with specific tasks but requires human oversight (a human in the loop) at key decision points. Think of it as a chain of AI-powered assistants that hand off work, like a baton, to each other with your approval. The system might draft emails, analyze data, or suggest actions, but a human must review and approve each step.

A true AI agent, by contrast, operates with genuine autonomy. It perceives its environment, makes decisions, and takes actions independently to achieve specified goals. The key difference? An AI agent doesn’t just assist, it acts. It can plan and execute multiple steps, adapt to unexpected situations, and complete complex tasks without constant human intervention.

We don鈥檛 have true agents yet. Yes, I鈥檝e experimented with ChatGPT Operator, Agent, and Manus, but they are not fully autonomous, and it would be reckless to assign them any serious work. However, the release of GPT-5 and the speed of improvements signal that agents will become ever more capable much more quickly.

The 4 core principles

There are four core principles 鈥� transparency, autonomy, reliability, and visibility 鈥� that must be adhered to when deploying true AI agents in law and other high-stakes fields. Let鈥檚 look at each principle in turn.

Transparency

Transparency means being able to observe what an AI agent does at every step. This isn’t just about logging actions, rather it’s about understanding the agent’s decision-making process in real-time.

Consider an AI agent assisting with legal research and case preparation. True transparency would mean the user could see which case law databases it consulted and understand why it chose certain precedents over others. In addition, the user would be able to track how the agent weighted different factors, such as jurisdiction, recency, and similarity. And the user also could observe the agent鈥檚 reasoning for distinguishing or applying specific cases.

Without transparency, we’re operating on faith 鈥� we might see outcomes but miss critical context about how those outcomes were achieved, which becomes especially problematic when agents make mistakes. Without transparency, we can’t diagnose what went wrong or prevent future errors.

For implementation, developers need to build comprehensive logging systems that capture and display, not just actions, but the agent鈥檚 reasoning as well. They should create dashboards that visualize decision trees in real-time, and design interrupt mechanisms that allow human inspection at any point.

Autonomy

Autonomy, the agent’s ability to act independently, is both the greatest promise and challenge of AI agents. True autonomy means the agent can initiate actions without explicit commands, adapt strategies based on changing conditions, make judgment calls in ambiguous situations, and recover from errors without human intervention.

The key is matching the AI鈥檚 autonomy levels to the risk profile of the work being undertaken. High-stakes decisions will likely require human-in-the-loop constraints, while less risky or routine operations can run fully autonomously. This calibration is an ongoing process, not a one-time setting. Legal ethical requirements also will help set the limits of an agent鈥檚 autonomy.

To design autonomy into the system, developers should establish clear boundaries and escalation protocols. They should define which decisions require human approval and which can proceed independently, while also building in periodic autonomy reviews to adjust boundaries based on performance.

Reliability

Reliability in AI agents goes beyond simple accuracy. It encompasses the answers to questions such as: Is the information the agent acts upon accurate and current? Do the agent鈥檚 actions consistently comport with ethical requirements and does the agent perform consistently across different contexts? And when things do go wrong, does the agent fail gracefully?

A dangerous misconception is equating autonomy with reliability. Just because an agent operates independently doesn’t mean its outputs are trustworthy. In fact, autonomous operation can mask reliability issues until it鈥檚 too late, and they cascade into significant failures.

To ensure reliability, developers need to implement robust testing frameworks that go beyond best-case scenarios. They should create adversarial testing environments, monitor for drift in performance over time, and establish clear reliability metrics tied to real-world outcomes.

Visibility

Visibility, often overlooked, might be the most critical principle. It refers to the scope of information available to an agent when it makes decisions.

When humans research a problem, they can cast a wide net, which leads them to follow unexpected leads and discover information they didn’t know they needed. AI agents, on the other hand, operate within defined parameters 鈥� they can only see what they’re programmed to look for.

This creates a fundamental limitation: AI agents make choices about what information to seek and process, potentially missing crucial context. These filtering decisions happen opaquely, creating blind spots a user might not even know exist.

To implement visibility, developers should map the full information landscape available to the AI agent, documenting what data sources are included and, crucially, what’s excluded. They should also build mechanisms for agents to signal when they’re operating at the edges of their visibility boundaries.

Overlapping interactions

Critically, these four principles don’t exist in isolation, rather they interact in complex ways, including:

• • Transparency without visibility shows us what an agent did but not what it missed. We might see every step of the agent’s process while remaining blind to alternative paths not taken.
  • Autonomy without reliability creates unpredictable systems that act independently but inconsistently. This combination is particularly dangerous in high-stakes environments.
  • Reliability without transparency gives us consistent outcomes but no insight into the process, undermining its credibility. The agent might work perfectly until it doesn’t, with no prior warning signs.
  • Visibility without autonomy creates systems that can see everything but act on nothing, becoming sophisticated analysis tools that still require human execution for every step.

The path forward with AI agents

Granted, true AI agents will live in a world we don鈥檛 inhabit yet, but they are coming along quickly. That means the future of AI agents isn’t about choosing between human control and machine autonomy. It’s about creating systems in which both can work together effectively, with clear principles guiding their interaction.

As we move forward, we must remember that every AI agent embodies a theory about how decisions should be made. The principles we embed in them will shape not just their behavior but our own expectations about reasoning, responsibility, and trust. In our rush to create agents that can act in the world, are we thinking deeply enough about the kind of world we want them to create?

In we鈥檒l take a microscope to GPT-5 and see how it ticks and what makes it useful.

Key highlights:

• • • Education and licensing misalignment 鈥� Legal education and attorney licensing are misaligned with the real-world skills and practical competencies new lawyers need to serve clients and address the nation鈥檚 growing access to justice crisis.

    • Strong support for licensing reform 鈥� There is strong momentum and support for reforming traditional pathways to legal licensure, according to research conducted by a body of chief justices and state court administrators.

    • Change will require leadership 鈥� Lasting, systemic change requires leadership and collaboration among state supreme courts, law schools, bar examiners, and the practicing bar.

For decades, cracks have widened in the nation鈥檚 promise of justice for all, with millions of people every year unable to find or afford legal help when they need it most. As the legal system in the United States faces a reckoning, one outline for change has emerged with the recently released (CLEAR), a body of chief justices and court administrators from a variety of states across the country. (CLEAR cited support from the 成人VR视频 Institute in the production of the report.)

The CLEAR group is calling for a radical change in how lawyers are taught and licensed. The report cites several factors driving the need for reform, including:

Increases in legal deserts and self-represented litigants 鈥� Judges in courtrooms across the country routinely see self-represented litigants, while so-called legal deserts, especially in rural areas, leave entire communities with few or no attorneys at all. Indeed, according to the American Bar Association, are considered legal deserts, with less than one lawyer per 1,000 people. As a result, most litigants are left to navigate a complex court system with inadequate or no legal assistance in family, probate and estate, housing, consumer, and criminal matters, according to the .

Declining interest in public sector work 鈥� The public interest sector, which includes civil legal aid, public defenders, and prosecutors, is buckling under the weight of crushing caseloads, stagnant federal and state funding, and a persistent shortage of lawyers. Indeed, students face numerous barriers to pursuing a career in public interest law, according to the CLEAR report, from less predictable career paths as compared to private practice, to a perceived lack of prestige in many schools, to the prospect of managing educational loans on a public interest lawyer鈥檚 salary.

Rapid technology changes 鈥� Compounding these challenges, advanced technology and especially AI are rapidly reshaping the legal profession. This, in part, is leading to that are essential for skill development because AI 鈥� which excels in tasks like legal research, writing, and drafting 鈥� now is handling work that had been historically assigned to associates and was a big part of how they learned their craft.

Defining practice readiness and minimum competence

Against this backdrop, the CLEAR report calls for overhauling how law schools educate attorneys and how bar admissions assess attorney readiness. More specifically, the report recommends a sharper, modern definition of practice readiness that more clearly defines the blend of knowledge, skills, and professional abilities that new lawyers must possess to competently serve clients from day one across four essential pillars. These pillars are i) foundational legal knowledge and analytical skills; ii) strong ethics and professionalism; iii) durable communication and interpersonal abilities; and iv) practical legal skills like advocacy, negotiation, and client management.

For the report, CLEAR surveyed of more than 4,000 judges, 4,000 attorneys, and 600 law students; and the committee鈥檚 findings consistently reveal that new lawyers struggle with practical legal skills, which include effective client communication, negotiation, and courtroom advocacy in addition to 17 other skills.

Feedback from survey participants points to the fact that these skills, which are crucial for the daily realities of legal practices, are not taught in law schools to a large degree. For example, only 7%听of experienced attorneys with more than five years of practice report that newly admitted attorneys, most of which are right out of law school, were very well or extremely well prepared to communicate effectively with clients. Likewise, 61%听of experienced attorneys said new lawyers were not well prepared or only slightly well prepared in negotiation, and 55%听of experienced attorneys said the same about new lawyers when it came to questioning and interviewing witnesses.

In addition, 66%听of judges say that new attorneys in their first five years of practice sometimes, rarely, or never competently conducted direct and cross examinations.

New pathways to licensure beyond the bar exam

Meanwhile, an additional insight from the CLEAR report highlights how the bar exam continues to focus heavily on theoretical knowledge and memorization, rather than the practical, day-to-day skills that define minimum competence. At the same time, the is more focused on foundation skills, including legal research, legal writing, and issue-spotting and analysis.

To address the dissatisfaction with the traditional bar exam, some states have been piloting innovative licensure pathways that better align with the skills new lawyers need. Such approaches include curricular pathways, such as in the in New Hampshire, and at the University of Wisconsin鈥檚 law school. Other methods are supervised practice models, such as in Oregon鈥檚 , , and temporary pandemic-era alternatives that provided graduates with the ability to prove their competence under the guidance of experienced attorneys.

Top recommendations for state supreme courts

The CLEAR group advocates for state supreme courts, as the profession鈥檚 primary regulators, to lead and foster innovation in licensure and practice readiness. The report urges state supreme courts to take such action as:

Lead collaborative efforts to realign legal education, bar admissions, and new lawyers鈥� readiness with public needs 鈥� State supreme courts are uniquely well-positioned to lead efforts to create a legal system that better addresses the legal needs of the communities they serve.

Encourage law school accreditation that serves the public 鈥� State supreme courts should encourage an accreditation process that promotes innovation, experimentation, and cost-effective legal education geared toward the goal of having lawyers meet the legal needs of the public.

Reform bar admissions processes to better meet public needs 鈥� This reform includes adjusting bar admission by setting passing scores based on evidence and piloting alternative pathways to passing the exam or equivalent assessment.

To put CLEAR鈥檚 recommendations for state supreme courts into practice, however, bold, coordinated action by law school administrators and the American Bar Association (as the accreditor of law schools) are critical as well. In particular, there is a need for expansion of experiential learning, such as clinics, externships, and simulation courses, to help students gain meaningful, hands-on experience and have direct responsibility with clients. In addition, aligning curricula with the realities of practice by integrating practical skills, ethics, and professional identity formation throughout, rather than relegating those factors to optional or add-on courses is another necessary reform.

Legal education and licensing must rapidly evolve to meet the nation鈥檚 urgent access-to-justice challenges, the CLEAR report notes. Law schools and state supreme courts must work together with renewed urgency and vision to lead this transformation. The failure to act by both law schools and courts means the justice gap in the US will only widen. Only with urgent, collaborative innovation to enact these changes can the legal profession deliver on the promise of justice for all in the decades to come.

You can access the full here