Leveraging Artificial Intelligence to Foster Improved Student Understanding and Scale Clinical Assistance for Underserved Populations

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Session Type: Lightning Sessions

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One seldom learns a subject as well as when one teaches it. Imagine the mastery demonstrated by successfully teaching a task to a machine. This session will demonstrate and invite attendees to use a suite of tech tools aimed at reinforcing student learning through the training and creation of AI applications. No prior technical background is required. Several modalities will be presented which instructors can adopt independently or in coordination, including: 1) playing an online issue spotting game that helps train an AI-based issue spotter used by nonprofits; 2) developing rules-based expert systems to help professionals, paraprofessionals, and lay people navigate complex legal issues and workflows; and 3) the merging of issue spotters with expert systems and document automation to eliminate repetitive tasks or produce interactive pro se materials. This session will provide a high-level overview of expert systems and machine learning (two historic approaches to AI development). It will then focus on the creation of AI tools as a method for forcing students to externalize and evaluate mental models of their work. It will focus on case studies involving the student creation of expert systems (primarily in the form of pro se document creation), as well as student involvement in the crowdsourcing of issue spotting to aid in the training of machine learning tools for legal nonprofits. It will touch on the appropriate use and scope of such tools, including issues of algorithmic bias. Additionally, attendees will have the opportunity to engage with demos of student work, help train an AI issue spotter, and potentially to create their own simple expert systems.