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Document Automation for Law Schools
Guide

Document Automation for Law Schools

Legal tech is the cornerstone of the future of law practice. If you’re an educator or student, we invite you to explore our series of exercises and tools, which law schools around the world have built into their core curricula.

By the team at Gavel
March 20, 2024
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In the next 10 years, 90% of legal services will be delivered online. Documate is the infrastructure on which online legal is being built. Documate was built by a team of lawyers and engineers who have been immersed in the legal field for the last decade. And we have some thoughts about how legal tech should be incorporated in law schools!

The series of tools below cover the span of legal tech, but we focus primarily on the following: (1) Document automation, (2) Expert systems, and (3) Legal products.

These are our areas of expertise at Documate, and we're happy to host a session with your law school class on these topics or give them student accounts to play around the system. Email us at [email protected].

Document Automation Training and Exercises

Hands-on building is the best way for law students to understand legal technology.  We provide educator and student accounts for law school classes, for which we recommend the following structure:

1. Get inspired.

We find that the best way to learn is to think about the "dream state" for your tech-enabled practice. There are many inspiring examples of how lawyers have built document automation to enhance their practice and built full-fledged legal tech products that have made them legal tech entrepreneurs. We invite you to review the case studies and guides below to visualize the possibilities:

-  Documate users have built legal apps in 23 different countries and 18 different languages. Our case guides, which we're always adding to, have convinced many a law student to become a legal tech entrepreneur.

-  Legal productization is where the legal field is headed. Read The Ultimate Guide on How to Build Legal Products, the definitive guide on building legal apps.

-  What have other students built? See videos of the finalists for our last legal app competition here.

2. Learn.

Students can learn the basics of using Documate (and how document automation and expert systems work) by reviewing the instructions below. Documate is a no-code system, which means users do not need to learn fancy syntax or coding languages to get started.

-  Start with Documate 101 - Crash Course here.

-  See how logic and decision trees can be used to add multiple layers of logic in this Invisible Logic course.

-  Learn better by reading manuals? Review the possibilities in our written Help Guide.

3. Do.

Practical exercises are always the best way for students to learn. We've created a set of games and hypothetical scenarios where students can put their knowledge to use directly on the Documate platform.

Go to Law School Document Automation Exercise 1

Documate is used by law schools around the world. Here are just a few of the law school programs using Documate to teach students about document automation and legal productization:

Non-Automation Exercises for Students

Outside of automation, here are a few of our favorite games and exercises for law students to learn about legal technology.

Learned Hands Game

Learned Hands is an issue spotting game.  You will spot potential legal issues in real stories originally created from a legal questions subreddit.  Read the stories, then say whether you see a particular legal issue (family law, consumer law, criminal law, etc.).

The game also functions as a research project.  As you play, you are training a machine learning model to spot people’s legal issues.  This model is planned be used to develop access to justice technologies that connect people with public legal help resources - linking the legal help guides that courts and legal aid groups offer to the people who are searching for help.

Artificial Intelligence Biases

Play this courtroom algorithm game to see how the US criminal legal system uses predictive algorithms to try to make the judicial process less biased.

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