Exploring the Digital Transformation of Generative AI-Assisted Foreign Language Education: A Socio-Technical Systems Perspective Based on Mixed-Methods

Abstract

This study investigates the complex dynamics and impacts of generative AI integration in foreign language education through the lens of the Generative AI-assisted Foreign Language Education Socio-Technical System (GAIFL-STS) model. Employing an integrated mixed-methods design, the study combines qualitative case studies and hybrid simulation modeling to examine the affordances, challenges, and implications of AI adoption from a multi-level, multi-dimensional, and multi-stakeholder perspective. The qualitative findings, based on interviews, observations, and document analyses, reveal the transformative potential of generative AI in enhancing language learning experiences, as well as the social, cultural, and ethical tensions that arise in the process. The quantitative results, derived from system dynamics and agent-based modeling, provide a systemic and dynamic understanding of the key variables, feedback loops, and emergent properties that shape the trajectories and outcomes of AI integration. The integrated findings offer valuable insights into the strategies, practices, and policies that can support the effective, equitable, and responsible implementation of AI in language education.

Keywords

• generative AI
• foreign language education
• digital transformation
• socio-technical systems
• mixed methods
• system dynamics