Promoting Better Dispute Decision-Making with an AI Tool Built on RPS Theory
79 Washington University Journal of Law and Policy 135 (2026), 27 pages.
This foundational article provides the theoretical basis for RPS Coach. It critiques traditional negotiation and mediation theories, and it explains why RPS Theory is a more accurate and practical alternative. It describes how RPS Coach was built using RPS checklists for mediators and lawyers to perform multiple tasks for many types of individual and institutional users. It identifies some risks that RPS Coach presents, but notes that these are relatively low risks compared with other AI tools because it is designed to suggest ideas, not provide definitive answers. It argues that promoting AI literacy is one of the best safeguards against potential problems created by AI tools.
A Practical Guide for Using the RPS Negotiation and Mediation Coach
SSRN (March 31, 2025), 2 pages.
This two-page guide explains who can benefit from RPS Coach, what tasks it can perform, and how it promotes good decision-making. It includes a direct link so people can access the tool immediately.
The Artificially Intelligent RPS Negotiation and Mediation Coach
Indisputably blog (March 16, 2025).
This post offers a concise overview of RPS Coach and shares reflections from a presentation at UC Law San Francisco.
The Artificially Intelligent RPS Negotiation and Mediation Coach
SSRN (March 10, 2025), 9 pages.
This article presents core principles of RPS Theory and practice and explains how RPS Coach integrates them into its knowledge base. It identifies key advantages over untrained ChatGPT and examines the Coach’s applications in dispute resolution practice, program design, and education.
RPS Coach is Biased – and Proud of It
SSRN (March 30, 2025), 4 pages.
This essay explores the values and design choices embedded in RPS Coach. It maps the structure of its knowledge base and the instructions that guide its responses. RPS Coach promotes good decision-making by mediators, lawyers, parties, and educators. It encourages users to adapt dispute resolution processes to the needs of actual parties, rather than rely on standardized models. It favors professional growth, responsiveness, and flexible systems over fixed scripts.