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

Key findings:

• • • Law schools as innovation hubs 鈥� Several law schools across the US are becoming laboratories for access to justice, using technology and partnerships to develop scalable legal tools that help underserved communities.

    • Technology-driven experiential learning 鈥� Students are gaining hands-on experience by building digital tools 鈥� like document automation, chatbots, and AI-powered self-service tools 鈥� that expand legal services and improve service delivery.

    • Systemic impact on legal education 鈥� These programs offer not just innovation at the local level but present an opportunity for a structural shift in legal education and justice reform more broadly.

Law schools are stepping into a critical role as laboratories for persistent access to justice challenges. As the legal system grapples with rising demand and constrained resources, many law schools are forging partnerships, launching clinics, and embracing technology to bridge the justice gap.

Experiential learning is increasingly important in the rapidly changing legal sector, and with technological innovation, it is helping to close justice gaps that traditional models have failed to address. By embedding students into tech-enabled, hybrid frontline legal services and having them work on building scalable, human-centered solutions, a growing number of legal education programs have embraced this dual imperative. Students learn the law and deploy it through digital tools, virtual services, and data-informed strategies that help them augment their abilities and maximize impact.

Building for scale with guided tools

Many law school-based tech clinics currently leverage technology not just to serve clients, but to test new methods of delivery. Early pioneers in this space include collaborations with Pro Bono Net and schools like Chicago-Kent College of Law and Suffolk University Law School. Using the LawHelp Interactive platform, students recently built guided interviews and digital self-help forms with tools like A2J Author and HotDocs to assist unrepresented litigants navigating thorny legal issues such as divorce, eviction, and domestic violence.

These tools 鈥� designed to be used independently by non-lawyers 鈥� exemplify a modern variation on experiential education in which students create scalable, public-facing tools with measurable impact.

For example, Western New England School of Law鈥檚 Center for Social Justice uses document automation and online in-take portals to provide services for criminal record expungement and LGBTQ+ legal support. Students provide targeted services at scale, reaching individuals who may otherwise be excluded from traditional legal systems due to transportation, financial, or cultural barriers.

As the legal system grapples with rising demand and constrained resources, many law schools are forging partnerships, launching clinics, and embracing technology to bridge the justice gap.

At Suffolk University’s Legal Innovation and Technology Clinic, law students collaborate with legal aid organizations and courts to develop scalable solutions for civil legal issues. Students are taught project management and computer programming to create powerful tools that directly assist unrepresented parties, including and online guided interviews which help people navigate processes such as .

Ohio State University鈥檚 Justice Tech Practicum brings together law students and computer science students to design, build, test, and refine technologies aimed at addressing access to justice issues. The program is currently working with the Self-Help Center of Franklin County to develop tools for tenants facing eviction.

Tech clinics as justice design labs

Law school clinics are increasingly functioning as innovation labs for system-level design 鈥� incubating, testing, and improving justice tools in real time. Suffolk University’s Legal Innovation and Technology Clinic is partnering with the American Arbitration Association to pilot tech-driven approaches to low-contest divorces and family law matters in Massachusetts. Students help design and test accessible digital tools that streamline dispute resolution processes.

At the , the Innovation for Justice program collaborated with the Alaska Legal Services Corporation to improve Benefactor, a digital tool that helps guide case managers, social workers, and community navigators through the Social Security disability application process. The Arizona UX for Justice team delivered a human-centered design roadmap for product refinement, legal empowerment, and broader implementation.

Legal Aid of North Carolina (LANC) is also tapping into the power of collaborative tech development. Through its Innovation Lab, LANC worked with law students from Duke and Vanderbilt universities to develop and refine its Legal Information Answers chatbot with students conducting auditing and user testing to optimize the client experience. Vanderbilt students also analyzed LANC lawyer workflows to identify how AI tools might improve staff effectiveness.

AI and data-driven legal empowerment

Law schools are also leveraging AI to improve access to justice, transparency, and user experience. VAILL, the Vanderbilt AI Law Lab, is collaborating with lawyers from the Legal Aid Society of Middle Tennessee, Vanderbilt Data Science Institute (DSI) students and staff, and courts to create and implement Day in Court, a tool to help unrepresented parties navigate court appearances successfully. The pilot will initially focus on small claims matters and provide a platform that can be replicated in other jurisdictions. VAILL and DSI also collaborated to create an advanced directive tool, powered by generative AI (GenAI), for Tennesseans, a technology that will serve as the model for a future suite of self-service life planning tools.

Law school clinics are increasingly functioning as innovation labs for system-level design 鈥� incubating, testing, and improving justice tools in real time.

The Stanford Legal Design Lab engages students in service design, user research, and AI strategy in partnership with public interest organizations like Legal Services Corporation, the American Bar Association, Los Angeles courts, and legal aid groups around the country. Students conduct community interviews, run workshops, and develop accountability frameworks for AI-powered justice. In one project, Stanford students collaborated with the NAACP to refine and scale their Housing Navigator, an eviction prevention pilot that can help tenants navigate housing instability.

Implications for legal education

These are just a few examples of law school programs that are reimagining the student鈥檚 role not simply as a temporary service provider, but as a developer of justice infrastructure. What distinguishes these programs is not just innovation at the local level, but the structural insight they offer for legal education and justice reform more broadly. They suggest a model in which:

• • • experiential learning is centered on service delivery;
    • technology is now integral, not peripheral, to legal education; and
    • students contribute to justice infrastructure, not just as one-time interventions.

These models also underscore the power of public-private collaboration. As more courts digitize their services, these student-built tools are increasingly integrated into formal legal processes. Organizations can expand their capacity without proportional increases in cost or headcount, while advancing digital literacy among students and clients alike. Law schools, in this context, are neither isolated nor purely academic 鈥� they are collaborators and facilitators in a broader ecosystem of justice.

As legal needs intensify amid economic and social strain, these programs offer more than isolated success stories. They present a blueprint for rethinking how legal services are delivered and who delivers them. By treating law students not only as future lawyers but also as present contributors 鈥� and by equipping them with technology to do so efficiently 鈥� these initiatives are helping to shift the access-to-justice paradigm from one of scarcity to one of scalability.

For policymakers, educators, and legal professionals, the message is clear: innovation doesn鈥檛 have to wait for graduation. It鈥檚 happening now 鈥� in clinics, classrooms, and cloud-based platforms 鈥� where tomorrow鈥檚 lawyers are building the infrastructure for a more just and accessible legal system today.

You can find more articles in our Scaling Justice series here

Historically, much of that third phase of learning involves relatively low-stakes and potentially dreary tasks, which have, nevertheless, become important teaching tools as fledgling lawyers grow into their new professional identities.听Today, however, generative artificial intelligence (GenAI) technologies pose a potential threat to this developmental model by fundamentally altering the types of work lawyers actually do.

The automation of routine tasks historically performed by first- and second-year associates results in a gap within the traditional talent pipeline, seriously challenging law firms鈥� conventional training models. After all, how are firms supposed to bring up senior associates if the work that traditionally transformed younger associates into more senior lawyers has been automated away?

This conundrum is not unique to the legal sector nor other professional services. Indeed, several insights can be gleaned from the engineering profession鈥檚 own history with technological automation and how it forced changes to a centuries-old training structure.

The extinction of the draftsmen

Up until the end of the 20^th century, draftsmen played a pivotal role in the engineering world as a necessary stepping-stone to the higher role of experienced engineers. These skilled individuals were responsible for translating engineers’ concepts and designs into precise technical drawings, which served as the blueprints for manufacturing and construction. Draftsmen had to meticulously hand-draw every detail, ensuring accuracy and clarity in their work without the aid of any computers.

Generative artificial intelligence technologies pose a potential threat to this developmental model by fundamentally altering the types of work lawyers actually do.

The typical career progression in engineering firms followed a clear path that many lawyers may find familiar: individuals would start as draftsmen, mastering the exacting intricacies of detailed drawing and design work. Over time, as they gained experience and honed their skills, they would advance to the position of designer. Designers held a more senior role, involving greater responsibilities such as conceptualizing and developing new products, systems, or structures. The transition from draftsman to designer was a gradual process, facilitated by years of hands-on drafting experience, which provided a solid foundation for more complex design tasks.

However, with the rise of computer-aided drafting (CAD) technology, which proliferated in the late 1970s and early 鈥�80s, the landscape of the engineering profession underwent a dramatic transformation. CAD systems automated many of the tasks traditionally performed by draftsmen, enabling engineers and designers to create, modify, and optimize designs with unprecedented speed and precision. This technological advancement rendered the role of the draftsman effectively extinct, as the need for manual drafting diminished.

The elimination of the draftsman role created a significant challenge for engineering firms. Without the traditional scut work of drafting to train new designers, firms could no longer rely on the progressive advancement that had been the method du jour of training designers for more than a century. Their traditional talent ladder with which they created more experienced designers had been shattered.

How did engineering firms adapt?

The solution to the extinction of their draftsman class that engineering firms eventually developed involved taking a muti-faceted approach which began with throwing new engineers into the deep end. Young engineers fresh out of college or trade school would go straight to the designer role, working on the advanced design processes right away rather than toiling for years on the traditional drafting tasks.

This approach, however, required a significant change in the training model, necessitating greater mentorship and supervision to ensure that new engineers acquired the skills and experience they needed to handle the complexities of their new roles. These fresh designers were, after all, working on projects that would traditionally have been years beyond their level of preparation, meaning the only way to ensure their proper completion was for more experienced hands to take a greater role in the young engineers鈥� training and supervision.

Legal training evolution will also require discussions with law schools about the new skills that lawyers entering the profession will need to succeed.

The benefits, however, dramatically outweighed the costs as senior designers had more time to invest because of their own automated assets, and inexperienced designers came online at a faster rate.

At the same time, engineering firms worked with educational institutions to rebuild the instructive structure of upcoming engineers, focusing less on drafting skills and more on technological education that was seen as essential for running CAD systems proficiently. Also, firms and schools placed greater emphasis on the more advanced engineering and design skills that the modern designer role demanded, emphasizing the importance of developing expertise in using sophisticated design software and understanding complex engineering principles from the outset.

In this way, engineering firms ensured that their new hires were not only capable of performing the high-level tasks required in modern engineering but also were well-supported as they transitioned into their new role 鈥� one which traditionally would have taken additional years of experience to reach.

Conclusion

The legal professional has already experienced some of the changes expected from AI-driven technologies, such as with digital research and cite-checking, albeit on a much smaller scale. Yet with those change being turbo-charged by impact of GenAI, dramatically changing traditional training models will be a significant undertaking for law firms, particularly if such changes require more involvement of partners and other senior lawyers, especially around mentoring and supervising the work of new lawyers. And similar to what occurred with engineering, this legal training evolution will also require discussions with law schools about the new skills that lawyers entering the profession will need to succeed.

All of this change will require law firms themselves to embrace a mindset of flexibility, understanding, and cooperation if they are to create for themselves the future senior lawyers and ultimate partners they desperately need.

You can find more of the challenges law firms face in hiring and retaining top talent here

Courses focus intensively on three core aspects of legal leadership: mindful and resilient leadership, adaptive leadership and ethical leadership. 鈥淲e believe these are bedrock themes for effective legal leadership,鈥� says Joel Barolsky, senior fellow at the University of Melbourne and one of the architects of the program. 鈥淚t goes well beyond what is traditionally taught in law school to create adjunct mindsets, knowledge and capabilities that can be important complements to legal skills.鈥�

The program does more than simply teach basic leadership skills. It helps lawyers identify the knowledge and capabilities required for legal leadership that is effective and fits within the framework of legal ethics. Considerable time is spent discussing how to deal with the rapidly developing knowledge base on legal leadership and management that is driving significant change throughout the industry.

Curriculum of legal leadership

While it鈥檚 important for participants to understand leadership theories, models, and approaches and how those factors can impact organizational performance, students also learn how to apply their own personal leadership capabilities within the context of their own teams and organizations. One of the key factors on which instructors focus is how to best apply these newly acquired skills and knowledge not only to benefit students鈥� own careers, but to enhance the effectiveness of their organizations as well.

Even if a practitioner doesn鈥檛 aspire to a management position, such as practice group leader, chief legal officer or managing partner, holding a deep understanding of business, communications and managerial leadership can help further a myriad of goals that may extend both inside and outside of an organization.

Courses focus intensively on three core aspects of legal leadership: mindful and resilient leadership, adaptive leadership and ethical leadership.

For example, research is suggesting it is increasingly important to young lawyers for their firm or organization to have a strong environmental, social and corporate governance (ESG) mission. However, advancing that mission at all levels 鈥� within an organization, engaging clients and partnering with third-party organizations 鈥� requires effective leadership.

While the Legal Leadership program is meant to help certificate holders develop leadership skills and take those skills back to their respective organizations, it is also expected to positively impact all aspects involving communications and relationships, including lawyer-client relationships.

鈥淭here鈥檚 a lot of enthusiasm for this program within a range of organizations, including law firms, corporate law departments and government,鈥� says Prof. Pip Nicholson, former Dean of the Melbourne Law School and newly appointed Deputy Vice Chancellor for People and Community. 鈥淢any already invest in professional development, but they love the opportunity to use a university setting to enhance leadership skills across their organizations.鈥�

Accessible learning

The six-month program is designed to be highly flexible, allowing participants considerable autonomy to customize study for their individual development needs. Class sizes are limited to 30 to better facilitate effective learning and interaction among participants.

Subjects are offered in an intensive executive education format for greater efficiency in learning. 鈥淲e鈥檝e intentionally designed it as a program with a manageable commitment and flexibility to enable a working practitioner to do it part-time,鈥� says Nicholson. 鈥淚t鈥檚 important that it be accessible and workable in order to fit in well within people鈥檚 busy work schedules.鈥�

The program is available to both Australian and international participants, with curriculum consisting of a combination of interactive live sessions, prerecorded lectures and discussion boards. Subjects are offered intensively to assist those with competing time commitments.

While many organizations provide professional development, often through their HR departments, the university setting brings together a broad set of resources and experienced instructors.

鈥淥ne of the real benefits from offering this in a university setting is the capacity to facilitate robust peer-to-peer discussion in our classrooms,鈥� says Barolsky. 鈥淵oung professionals will be able to talk across different contexts and address the complexity of the issues from the point of view of a government lawyer, an in-house lawyer, a private practice lawyer and so on. They will develop a broader perspective on what effective leadership looks like across the legal sector as a result of that.鈥�