Document Type
Article
Publication Date
2026
Abstract
This Essay examines whether generative artificial intelligence (GenAI) can lower the technical, time, and cost barriers that have limited the use of game-based learning (GBL) in legal education. It first considers the science of learning, focusing on spaced practice, retrieval practice, and concrete examples, and explains how GBL can incorporate these strategies. It then evaluates four general-purpose GenAI tools—Perplexity, Gemini, ChatGPT, and Claude—by testing their ability to state legal rules accurately and create games that teach personal jurisdiction.
The results demonstrate both GenAI’s promise and its limitations. Although all four tools incorporated core concepts of general jurisdiction, each omitted at least one recognized basis, underscoring the need for independent verification. The tools also varied considerably in the game elements and supporting materials they generated. Drawing on these results, the Essay identifies prompting practices centered on context, instruction, input data, and expected output that can improve the accuracy and usefulness of GenAI-generated learning games. It concludes that GenAI offers a viable way to expand GBL in legal education, provided users use detailed prompts, verify generated content, and remain mindful of evolving copyright and privacy concerns.
Recommended Citation
Nicole Belbin, GAmIfication: The Viability of Using GenAI to Create Games to Teach Legal Concepts, 2 JACKSONVILLE UNIV. L. REV. 126 (2026)
