Operationalizing Copyright for AI Visuals in Clinical Legal Education

Abstract

The rapid expansion of generative artificial intelligence in visual content production has intensified legal debates on authorship, originality, and the permissible boundaries of stylistic imitation under copyright law, as large-scale automated generation increasingly blurs the distinction between protected expression and unprotected style. This article examines the doctrinal ambiguity surrounding AI-generated visual works, particularly with regard to stylistic emulation, market substitution, and the potential erosion of creators’ economic and moral interests. Employing a doctrinal-normative legal method complemented by comparative and policy-oriented analysis, the study formulates operational criteria to distinguish lawful stylistic inspiration from infringing expressive replication by translating core copyright principles, including the idea–expression dichotomy, originality, and substantial similarity, into practical analytical tools. The findings indicate that, while existing copyright doctrine remains normatively relevant, it requires methodological operationalization to address the scale, speed, and distributive impacts of generative technologies on creative labor markets. The article further proposes integrating these doctrinal and policy considerations into clinical legal education through problem-based learning models to strengthen students’ doctrinal competence, technological literacy, and normative sensitivity. It argues that effective governance of AI-generated visual works demands a holistic framework combining doctrinal clarification, impact-oriented policy evaluation, and educational reform in order to foster a more adaptive and socially responsive copyright regime in the age of generative artificial intelligence.