Weaving Pathways for Justice with GPT: LLM-driven automated drafting of
  interactive legal applications
Quinten Steenhuis David Colarusso Bryce Willey
Can generative AI help us speed up the authoring of tools to help
self-represented litigants?
  In this paper, we describe 3 approaches to automating the completion of court
forms: a generative AI approach that uses GPT-3 to iteratively prompt the user
to answer questions, a constrained template-driven approach that uses
GPT-4-turbo to generate a draft of questions that are subject to human review,
and a hybrid method. We use the open source Docassemble platform in all 3
experiments, together with a tool created at Suffolk University Law School
called the Assembly Line Weaver. We conclude that the hybrid model of
constrained automated drafting with human review is best suited to the task of
authoring guided interviews.