Applying the SAMR Model to AI-Enhanced Business Language Instruction: Comparative Insights from German and Spanish Contexts

Background: The SAMR model offers a structured way to analyze how technology reshapes teaching and learning. In the context of Generative Artificial Intelligence (GAI), it provides a productive lens for reconsidering how language instruction - especially within Languages for Specific Purposes (LSP) - evolves in response to AI integration. Purpose: This article examines the use of AI-driven tools in Business German and Business Spanish across two contrasting settings: higher education and corporate training. It aims to build a comparative perspective that highlights how pedagogical intentions and constraints differ across these environments and what that means for responsible and effective AI integration. Conceptual Contribution: We propose that the SAMR model can be reframed specifically for Generative AI in Business LSP to clarify how instructional practices move between enhancement and transformation. Rather than reporting empirical findings, the article argues that AI-enabled personalization, collaboration, and authentic task design can be interpreted as occupying distinct SAMR levels. This conceptual reframing distinguishes between uses of AI that streamline existing practices and those that fundamentally rethink language learning experiences. Implications: By repositioning SAMR as a flexible analytic tool rather than a descriptive taxonomy, this article contends that educators can better anticipate both opportunities and risks in professional language instruction. The argument advances a foundation for integrating GAI in ways that support pedagogical innovation while safeguarding equity, integrity, and quality in Business German and Business Spanish education.