Hi there! The mission of Joy Legal is to bring JOY and happiness to the process of studying for the pre-law exam in the United States. We do this by creating fun, short (5 min or less per game) micro chunks of learning to help you increase the skills you need to be able to be successful on the pre-law exam. This website is being developed as a for-profit business with the intent and focus of eventually - pending interest and sign ups - benefiting a nonprofit. We are in the beginning stages and hope that students - and lawyers to be - will gain tremendous value from our content and love our platform. We aim to do good through education. We hope to eventually have a full pathway for students - similar to Duo Lingo - to help learners efficiently study for the pre-law exam. The pre-law exam is a skills based test, and we believe that anyone can learn the skills necessary to succeed.
About the Founder This website is founded by Shannon Gunn, jazz trombonist from DC. She happens to have experience as a music teacher, instructional designer, LMS administrator, eLearning developer, and a non profit executive in addition to her musical skills. As she was studying, she found herself getting angry and frustrated at the learning process. Specifically, it seems inefficient to have to learn from mistakes, usually by reading or watching 3 - 10 minutes worth of content to understand why. As she studied, she looked at questions and tried to determine why she got them wrong. What was the skill she was missing? Then she went to AI models and created drills around those skills. As she did this, she realized she could potentially help others do the same, and thus this website was born. There are no LSAT questions on this website. All content is 100% AI generated, edited, and then looked over by a lawyer. As a founder, and an instructional designer, Shannon separated the skills from the content and then is working on presenting the information in a spiral curriculum while leveling up Blooms and Depth of Knowledge to increase the cognitive rigor matrix.
