01 · Litigation Training

Trial Advocacy Training, Under Real Pressure.

Most evidence training stops at multiple choice. Trial Trainer doesn't. Built with trial attorney Priya Chaudhry, it places students in a simulated courtroom — judge, witness, opposing counsel — where they must lay a foundation, field objections both valid and invalid, and hold their position, all grounded in Evidentiary Foundations by Edward J. Imwinkelried. Every AI response passes through a five-tier review process, including regular evaluation by a panel of trial lawyers, before it ever reaches a student.

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Attorneys have under three seconds to object in a real trial. Trial Trainer is built for that clock.

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Rehearse every argument
before it counts

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Trial Trainer — simulated courtroom demo

Case Study · Trial Trainer

Teaching law students to argue under pressure.

An AI-simulated courtroom — built with trial attorney Priya Chaudhry — trains law students and junior associates to lay evidentiary foundations and handle objections in real time, where knowing the law and performing it are two different things.

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To object in a real trial — no time to reflect

5-tier

Validation before content reaches a student

85%+

Baseline AI-accuracy target on evaluations

At a glance

Commissioned by: Priya Chaudhry, Esq. — Forbes America’s Top Lawyer 2025
Domain: Legal education · trial advocacy & the rules of evidence
Next2AI role: Technology and research & development partner
Grounded in: Evidentiary Foundations (13th Ed.) — Edward J. Imwinkelried
Status: In development — text simulation now, real-time voice next

The challenge.

Legal education assumes that if students learn the rules of evidence, they’ll know how to apply them. That breaks down the moment a student stands up in a real courtroom. Trial work allows no time for reflection — an attorney has less than three seconds to raise an objection or respond to one. Students can recite the requirements for admitting evidence and still freeze when opposing counsel objects in real time.

Rules are learned, never performed
Under 3 seconds to object
Courtroom pressure can’t be staged in class
Students freeze when counsel objects

Why existing tools fall short.

Gamified tools

Trade professional rigor for engagement mechanics that don’t reflect the seriousness of courtroom practice.

Static quizzes

Test recall, but never place a student under any real pressure.

Neither replicates the actual experience of trial — the back-and-forth with a witness, the sudden objection, the need to think and speak at once, with no do-overs.

The solution.

Trial Trainer drops students inside a simulated courtroom. Rather than answering questions in isolation, they conduct an examination step by step — establishing a witness’s foundation, offering evidence, and handling objections as they arise.

Judge

Rules on objections in real time

Witness

Testifies while the student lays the foundation

Opposing counsel

Objects — sometimes rightly, sometimes not

Valid & curable

The student must recognize the gap and repair their foundation.

Invalid

The student must argue why the objection should be overruled.

Opposing counsel doesn’t always object correctly — just like real trials. Immediate feedback names the exact foundational step missed or handled well, so students learn not just whether they succeeded, but why.

Inside the first scenario.

Flagship scenario · “The Parking Lot”

A junior defense associate must admit a photograph — an alibi exhibit — into evidence through the testimony of a neighborhood witness. It tests the full arc of laying a photographic foundation under realistic conditions, with opposing counsel challenging every step.

Built for accuracy.

Because the platform trains future lawyers, accuracy is a non-negotiable requirement, not a feature. Every scenario and AI evaluation is grounded in Evidentiary Foundations (13th Ed.) — the field’s authoritative text. Content passes five tiers of validation before a student ever sees it:

01
Automated citation checks
Every claim checked against citations in the source text.
02
Expert panel review
Chaudhry and two trial lawyers blind-review AI evaluations, watching gray-area objections.
03
Dual-model verification
Two independent AI reads of the same objection; disagreements flagged for humans.
04
Confidence scoring
Any low-confidence response is routed to a human reviewer.
05
Red-team testing
Trial lawyers probe the system with edge cases before students see it.

Design targets — the platform hasn’t launched yet: a baseline AI accuracy of 85%+ on evaluation scenarios, and measurable improvement in a student’s objection accuracy by their third session.

Status & what’s next.

Trial Trainer is in development, with its core AI evidence engine and a text-based simulation as the present focus.

Now — text-based simulation on the core AI evidence engine
Next — real-time voice: speak objections aloud, under the clock
Later — further phases, defined by what the first stages teach

Priya Chaudhry, Esq.

Forbes America’s Top Lawyer 2025 · ~20 years teaching trial advocacy at Harvard · 50+ jury trials in complex criminal cases

Trial Trainer is commissioned by Chaudhry. Next2AI serves as the technology and R&D partner, building every scenario and every piece of AI feedback in close collaboration to reflect real trial practice.

Knowing the rules isn’t the same as performing them.
Trial Trainer closes that gap.

Talk to Next2AI →

Trial Advocacy Training, Under Real Pressure.

Rehearse every argumentbefore it counts

Rehearse every argument
before it counts