Key highlights:

• • • Build on internal momentum rather than top-down mandates — Dean Kalb backed faculty who were already experimenting with AI, embedding shared learning outcomes into the legal writing program first before expanding to other courses.

    • Empower students to shape the school’s AI policy — Dean Kalb formed a 15-person student advisory group that surveyed one-third of the student body and produced AI principles that directly influenced school policy.

    • Create opportunities to get students collaborating with faculty — Efforts by Dean Kalb uncovered shared concerns of faculty and students, underscoring that students often know AI tools better than faculty and creating a co-learning opportunity in the classroom.

In her first six months as dean at the University of San Francisco (USF) School of Law, Johanna Kalb heard the same message from alumni across sectors: Those students entering law school today would step into a profession that looks meaningfully different from the one that exists now.

So, in her first move to translate that urgency into institutional change, Dean Kalb got behind those faculty members who had already started building toward that future.

Start with what is already in motion and invite others in

Dean Kalb started with the efforts that Profs. Nicole Phillips and Megan Hutchinson had already been doing by conducting their own experiments in their classrooms and building their own tools.

Dean Kalb’s first step mattered as a strategic choice. Rather than convening a task force or commissioning a study, she identified the faculty who had credibility with their peers and gave them resources and institutional backing. In this way, USF was able to embed shared AI learning outcomes across its legal research, writing, and analysis program in the second semester of the 2024-‘25 academic year.

The decision to focus on this program was deliberate because it built upon existing internal momentum and fit into the course’s existing goals. The structure of the legal research and writing program — with faculty having autonomy while supporting each other — also made the integration work by providing natural support.

Expand through optional workshops before adding mandates

Over the following summer in 2025, Profs. Phillips and Hutchinson ran optional hands-on workshops for the broader faculty. “Faculty learn from other faculty,” explains Dean Kalb. “They don’t want a vendor to come in and sell them. They’re not interested in having somebody from central administration try to tell them how they can teach better. But listening to a colleague who really understands the work that they do is very helpful.”

Indeed, some faculty showed up, were excited by the possibilities, and began integrating AI learning outcomes into their elective courses. This voluntary uptake created a visible proof of concept before any additional requirements or mandates were introduced.

Alongside the workshops, Dean Kalb also expanded AI learning outcomes into two required courses on evidence and professional responsibility. The professional responsibility inclusion was straightforward given the ethical dimensions of AI use in legal practice. One colleague, Prof. Tiffany Li, had already been building those outcomes into her section and was willing to share her approach with other faculty members who were teaching the same course.

Give students a formal role in shaping the direction

When USF rolled out access to the AI platform Claude across students and faculty, the response was more complicated than Dean Kalb anticipated. Feedback from students at USF — a Catholic Jesuit institution with a strong social justice identity — raised questions about AI’s social, environmental, and democratic impacts.

Dean Kalb intentionally chose to use the students’ feedback to involve them. With the help of another alum, who has deep experience in evaluating and implementing emerging technologies, Dean Kalb convened a student group to develop a set of draft principles for AI use at USF Law. The students conducted structured interviews with faculty, staff, and students, resulting in the creation of a survey in which approximately one-third of the student body participated. The student group drew on these results to draft a series of AI principles and presented them to faculty, staff, and other students. Ultimately, the principles were adopted by the faculty.

What came out of that process has already begun to shape the law school’s AI practices in concrete ways. For example, a faculty technology advisory committee with student representation has been formed to implement the principles to ensure transparency and ongoing oversight. The school also has begun exploring ways to engage with AI that reflect and enhance its social justice mission.

We now have a shared sense of where the community is and what our concerns are. That allows us to speak in a common language as we talk about why and how we’re doing this.

The more significant outcome, Dean Kalb says, was the discovery of shared concerns among faculty and students that AI would erode critical thinking rather than develop it. “That was probably the most helpful part of the whole process,” she says. “We now have a shared sense of where the community is and what our concerns are. That allows us to speak in a common language as we talk about why and how we’re doing this.”

Commit to sharing in the learning

Dean Kalb’s suggestion for her peers and faculty is to integrate AI tools into their own lives, which would allow them to better ����������keep pace with technology that is moving faster than any curriculum committee can match. “It’s hard to regulate and teach these tools in the abstract,” she explains. “I’ve found that playing around with them in my personal life — where the stakes are low — has helped me come up with ideas for their use at work, and that in turn, means that I notice their evolution.”

For a profession built on expertise and the authority that comes with it, this mindset requires a particular kind of intellectual honesty. Some students are beginning to arrive at law school with more familiarity with AI tools than their professors, Dean Kalb adds, and this may offer an opportunity to shift the classroom dynamic toward co-creation, in which faculty and students are building knowledge together rather than transmitting it in one direction.

This change in perspective can, turn the stress of “keeping up” into the more enjoyable experience of collaboration, she says.

You can find out more about the impact of AI on legal education here

Key highlights:

• • • The limits of the current system and good intentions — While the justice gap is not the fault of legal educators, their good intentions alone cannot close a systemic gap that requires new models of training and delivery designed for the long term.

    • A healthcare model for legal services is needed — Just as the healthcare industry relies on physicians, nurses, and physician assistants, the justice system needs a wider spectrum of trained and regulated legal providers; and American law schools are best positioned to educate, license, and oversee them.

    • States prove the model works — Alaska, Utah, and Arizona have already developed programs that train and certify non-lawyer legal service providers to help individuals navigate courts and address common legal issues, offering a replicable framework for those states willing to open regulatory doors.

Our nation’s healthcare system has wisely evolved past being one built on doctors alone. Yet in the legal industry, access to services remains largely tethered to a lawyer-only model that leaves millions of people unable to secure the help they need. Every day, tenants face eviction without representation, parents navigate custody disputes alone, and workers struggle to secure employment benefits or resolve workplace disputes because they cannot pay for legal counsel.

Legal professionals need to work together to create a broader, smarter, and more efficient legal workforce that can meet the public’s legal needs while maintaining the United States’ current legal standards of excellence. American law schools are best positioned to lead this effort; however, they will need to partner with regulators to educate, license, and oversee new categories of legal service providers who, like nurses and physicians’ assistants, can help expand the public’s access to critical support.

Preserving excellence while expanding access

American legal education has long been the global gold standard, producing leaders in law, politics, and business. Its rigorous curriculum, emphasis on critical thinking, and commitment to developing practical problem-solving skills have established a framework that many systems around the world aspire to emulate.

While meaningful innovations have taken place in legal education over the years, many are best characterized as refinements to the existing model rather than significant reforms. For example, curricular options today are more likely to include a wider variety of subject areas and teaching methods, however, most US legal education is still delivered through an in-person, full-time, three-year post-graduate Juris Doctor (JD) degree. While the overall quality of American legal education is exceptional, it is not filling our nation’s need for justice work.

The consequences are increasingly difficult to ignore. Low-income Americans receive no or insufficient legal help for 92% of their substantial civil legal problems, according to the Legal Services Corp.’s report. As a result, in many court systems, self-represented litigants have become the norm rather than the exception, whether the legal challenge involves housing, consumer debt, or family stability.

This is not the fault of legal educators, who often go above and beyond to help bridge the gap through the provision of free legal services and other efforts. Even so, it is the responsibility of legal educators to assist in designing and supporting new models of training and legal delivery to systemically narrow the gap for the long term.

Innovation beyond fine-tuning

Addressing this persistent and growing issue will require more than fine tuning. Instead, to meet the demands of a society increasingly characterized by inequality, social division, and complex interdisciplinary problems requires change that will better prepare our justice system for the future.

To get there, legal educators may have to sacrifice one part of what has long defined them: homogeneity. While a degree from a more elite law school is certainly rewarded in the entry-level employment market, the legal education provided at most of the accredited law schools in the US is more alike than different.

For law schools to help close the justice gap, increasing institutional pluralism is essential. Law schools can and should differentiate themselves by developing tailored solutions to address specific justice challenges within their reach. For example, Medical-Legal Partnership Clinics at and help low-income clients address legal issues that can impact their health outcomes. And students at the University of Arkansas School of Law provide assistance to small businesses, nonprofits, and rural municipalities that often cannot afford legal counsel though the university’s Community and Rural Enterprise Development Clinic.

To be sure, law schools cannot and should not do this alone. Law school deans have rightly encouraged legal education’s accreditation process to improve regulatory flexibility and promote responsible change. As a result, many schools are developing high-quality online programs that offer both access and excellence. These programs may expand the pool of lawyers over time, but they remain largely focused on JD education rather than the broader workforce that will be needed to improve the public’s legal health.

A framework for responsible expansion

To enhance access to justice, the legal profession needs to move beyond “educating lawyers” alone and expand into teaching law more broadly. The traditional JD degree will continue to be vital to our legal system; but just as healthcare relies on physicians, nurses, physician assistants and other licensed professionals, the justice system needs a wider spectrum of trained and regulated providers.

To get there, states must open their doors to a wider range of legal services providers. Unfortunately, many states — often for political reasons — continue to resist allowing limited-service legal providers to handle routine but still important legal needs.

Models for this approach already exist. , , and each have developed programs that train and certify non-lawyer legal service providers to help individuals navigate courts, understand their rights, and address common legal issues involving housing, family law, public benefits, and debt.

If state courts and legislators are serious about closing the justice gap, they should begin by opening their regulatory doors to these alternative legal providers, while providing responsible licensing and oversight mechanisms in collaboration with law schools in their state. If those doors are open, law schools can and will step through. Many law schools already have innovative master’s degree programs that are aimed at law-adjacent fields such as government contracts, human resources, compliance, and more. These non-lawyer educational programs can easily be tailored for alternative legal providers.

Keeping legal education in the hands of American law schools will properly balance access and excellence, ensuring the public continues to be served by qualified practitioners. Law schools have the skilled faculty, ethical underpinnings, and institutional infrastructure that’s needed to train and oversee the next generation of justice workers.

A robust justice system needs a full spectrum of professionals to meet society’s legal needs, much as our healthcare system relies on a range of trained providers. Until we build such a structure, the justice gap will remain exactly where it sits today, to the detriment of many citizens.

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

Key highlights:

• • • Design before you build— A professor at the University of San Francisco School of Law maps out the student interaction and learning objective before touching any platform — working backwards from there toward the desired outcome.

    • Constraints protect the learning process — Every tool is deliberately engineered to withhold answers thereby forcing students to lead the process and do the thinking themselves.

    • Low-stakes practice, high-stakes results— Students who rehearse privately with AI tools arrive in class more confident and can point to those simulations as real skills in job interviews.

The image of a law student buried in case books still rings true, but at the University of San Francisco (USF) School of Law, there is a new kind of study partner in the room. AI-powered assistants are helping students practice the Socratic method in private, master Bluebook citations without a professor, and simulate client interviews before setting foot in a real law office.

These tools, constructed as carefully designed learning environments, have been built by , co-director of the legal research, writing, and analysis program at USF, who openly admits she is not a tech person.

Over the past two years, Prof. Phillips has built several AI-powered tools that include a case brief helper, a mediation bot and an employment law counseling coach. Using platforms accessible to anyone willing to think carefully about pedagogy, she outlined the replicable steps for other law schools to follow, including:

Step 1: Start with a problem — The single biggest mistake faculty makes is starting with technology. “It can’t just be ‘Let’s use AI!’ There has to be a specific learning outcome,” Prof. Phillips says, adding that every tool she has built began with a concrete student frustration or gap. For example, students wanted more ways to practice Bluebook citations with real feedback, so she built a Socratic method tool after observing that student anxiety about the technique was interfering with their ability to demonstrate what legal knowledge they knew.

Step 2: Design the interaction before the build — Once the problem is clear, Prof. Phillips says she maps out the student experience before touching any platform. “I’m really thinking about what I want the students to get out of it and then working backwards from there.” This means deciding whether the tool should help students explain a concept, revise a draft, or respond to follow-up questions under pressure. Crucially, this design-first approach also forces the builder to define constraints. In fact, none of Prof. Phillips’s tools will give a student the answer; instead, the student must lead, and the tool follows and pushes back.

Step 3: Build in the constraints — The most important step in the build process is to take the risk of AI providing answers and engineer it out of the tool entirely. The Bluebook Citation Bot, for instance, will never produce a complete citation on demand. Instead, the goal is for students to understand why a citation is constructed the way it is. Similarly, the Socratic Method assistant is designed so that students must drive their own thinking and sit with the same discomfort that arises in a real classroom, but in a private space in which the stakes are lower.

Step 4: Try to break the tool — Before any tool reaches a student, Prof. Phillips tests it exhaustively: first, by feeding it incorrect law to see if it pushes back; and then, by probing every way it might accidentally give away an answer. “I do a lot of testing and breaking and then rebuilding,” she explains.

Step 5: Pilot and iterate — When a tool is ready, Prof. Phillips tells students what it is designed to do, what she hopes they will get out of it, and that they may find errors. To address any tool’s mistakes, she invites students to bring the errors to her. This improves the tool through real-world feedback that no solo testing can replicate, and it repositions students as collaborators in the learning design rather than passive recipients of it.

Of all her tools, Prof. Phillips considers the Socratic Method assistant the most consequential. For first-generation law students especially, the Socratic classroom can feel less like a learning environment and more like a barrier. “Competence is often mistaken for confidence,” she says. “The opportunity to practice being wrong privately is really important.” Students who use the tool arrive in class more willing to participate. For those who use her experiential simulation tools, she describes how students can point to their experience with them in job interviews noting that they have practiced these skills.

However, the biggest barrier to faculty building their own tools is the mistaken belief that it requires technical expertise. Admittedly, the hard part that Prof. Phillips insists on is the design. Her advice to her peers, however, is to start with a problem your students have, work backwards from what you want them to be able to do, build in the constraints that protect the learning, and then, break it before they do.

Learn more about the AI and Future of Legal Practice initiative here

Key highlights:

• • • The theory-practice gap is now an AI-era crisis— 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— 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— On-demand programs led by expert faculty are available to help students sharpen pro bono skills, understand the use of AI in today’s 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’s 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: “When 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,” Sonday says. “This is work that has traditionally formed the foundation of junior associate training.” The skills AI cannot replicate — such as judgment, issue spotting in ambiguous situations, client communication, and ethical decision-making — 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 — and that’s generally a lawyer’s role.”

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,” she says. “Integrating 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’s 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��redesign must start now — One law school’s 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 — 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 — 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 — the document review, contract markup, research memo creation — 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 — that is to think and act collaboratively.

Rethinking the curriculum before AI does it for you

leads IE Law School’s AI initiative and is steering the school’s 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?

“Layering AI tools on top of an unchanged curriculum serves no one,” Perez-Llorca explains, adding that without answers to the fundamental questions, “you are just adding technology to a structure that was never designed to handle it.”

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’s 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’s 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. “It [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,” James explains, adding that the AI tutor will pull any student who is struggling deeper into the material until the learning outcome is achieved. “The conversation never stops until the learning does.”

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 — 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 — Investigate with AI on your own by starting small with a pilot project.
    • Strategically assign where AI goes — 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 — Map your law school’s 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 — Make experiential and simulation-based learning central to the curriculum.

“The choice is between dealing with this crisis by design or by default,” 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 — 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 — 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 — 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 Thomson Reuters Institute white paper.

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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 Thomson Reuters Institute’s “2026 Law Student Pulse Survey” by filling out the form below:

Key insights:

• • • Lawyers need two types of judgment — 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 — 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 — 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’ve 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.

“The whole model that corporate clients would subsidize the learning of junior lawyers is all going away [because of AI],” says , founder of Creative Lawyers, a consulting and advisory service dedicated to transforming the future of legal practices. “Corporate clients already hated it, and now they have a way to say, ‘I’m absolutely not paying for this.’”

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’s 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,” Mack states. “It’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.” 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. “If that translation doesn’t happen, the legal work doesn’t have much effect,” 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 — judgment — 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.

“What humans bring to the partnership with AI is judgment,” Mack says, demonstrating the kind of clarity that tends to arrive only after years of building things that work. “This is not optional — it is mission critical.”

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

Key insights:

• • • AI’s greatest strength in criminal justice is pattern recognition— 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— Legal professionals are integral to evaluating AI-generated outputs, especially when decisions affect evidence, warrants, and individuals’ constitutional rights.

    • The most effective model is human/AI collaboration— 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 — 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��, a joint effort by the National Center for State Courts��(NCSC) and the Thomson Reuters 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,” said Mark Cheatham, Chief of Police in Acworth, Georgia, during the webinar. “It 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 — 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,” Judge DuBois explained. “But 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’s 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’ rights are on the line. “A human cannot just rely on that machine,” said Joyce King, Deputy State’s Attorney for Frederick County in Maryland. “You 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 �������� Special Services (TRSS) echoed that sentiment with a principle that cuts across every application of AI in the justice system. “The number one thing… 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’s 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’s 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 — not against them — 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 — 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 — 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 — 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��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 — 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 28–29. It’s virtual and completely free — 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 — 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 — 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’s 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 — 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’s 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’s 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��. He is “The AI Law Professor” and writes this eponymous column for the Thomson Reuters Institute.

Key takeaways:

• • • AI threatens traditional lawyer development — 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’t agree on what constitutes “legal judgment” — 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 — 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 Thomson Reuters 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 “… to accelerate the development of legal judgment early in lawyers’ careers.”

The challenge is that each part of the profession — law schools, employers, state supreme courts (as regulators) — 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’s 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’s 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 — 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 — This means understanding what level of effort, precision, and risk each matter requires and matching responses to the stakes involved.
    • Reasoning through uncertainty — 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 — This includes knowing which legal authorities are most suitable and being able to assess their persuasive value.
    • Ethical judgment under pressure — 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’s 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 — up to $15 billion-plus annually — 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’s supported by an assessment of a small percentage of the legal industry’s 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 — law schools, employers, state supreme courts — 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’s 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. “Maybe… 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’s upcoming 2026 Future of AI & Technology Forum here