Abstract An enduring access-to-justice crisis leaves most low- and middle-income people without meaningful assistance for civil legal problems. In response, several U.S. jurisdictions have experimented with licensing legal paraprofessionals—such as Limited License Legal Technicians (LLLTs)—to provide a circumscribed set of services directly to the public. Using Washington State’s pioneering LLLT program and its successors as a case study, this Article argues that paraprofessional reforms have under-delivered because they replicate key features of the traditional professional model: substantial educational prerequisites, supervised practice requirements, and high-stakes examinations that raise entry costs, limit supply, and constrain scalability. The Article contends that modern AI changes the production function of routine legal work—particularly client intake, document preparation, and the translation of facts into legally relevant narratives—yet AI deployed directly to consumers poses serious risks, including error, bias, confidentiality threats, and jurisdictional mismatch, and it cannot reliably identify when a matter requires escalation to a lawyer. The Article therefore proposes an “AI–paraprofessional fusion” model: purpose-built, jurisdiction-specific AI tools paired with lightly trained human paraprofessionals who provide process guidance, verify and quality-control outputs, and triage cases for escalation when warranted. Finally, because unauthorized-practice rules are state-created constraints that helped produce today’s scarcity, the Article argues that the AI infrastructure enabling this model should be developed and maintained as a public good—auditable, updateable, and broadly accessible—rather than left solely to private market incentives. This approach offers a scalable path for United States jurisdictions—and potentially others—to expand competent, lower-cost legal assistance while preserving safety through human oversight and clear escalation channels.
Ray Worthy Campbell (Wed,) studied this question.