Integrating Generative AI into the language curriculum: lessons from corpus-based data-driven learning

This paper reflects on the changing practices of language curriculum in the era of Generative AI (GenAI). We focus on insights from data-driven learning (DDL) and its integration into the language curriculum, and explore its potential to inform our understanding of GenAI for curriculum enhancement and (re)design. The presentation aims to bring attention to the recently formed Language Curriculum SIG in BAAL.

Since the introduction of GenAI to the public in late 2022, there has been a lot of discussion and debates about its use in education. GenAI uses vast quantities of data and powerful machine learning models to create original content. It requires minimal technical knowledge from users as the interaction with the technology is through natural language input and output. However, our understanding of GenAI is still evolving, and for the majority of language teachers, finding a way to effectively leverage this new technology in their practice is challenging.

This presentation draws from over two decades of corpus-based DDL research and practice on the language curricula in the UK and in Germany. Our findings suggest that the exploratory and inductive learning approach from DDL can be applied to GenAI integration into the language curriculum. We will propose a model for curriculum enhancement and (re)design, which allows language teachers to evaluate and operationalise this endeavour.

We will argue that language teachers do not have to ‘reinvent the wheel’ when it comes to GenAI. Pedagogical tasks such as providing context-specific language examples, creating personalised learning content, are established practices in DDL. They provide a suitable space for the integration. We will also address the praxis of this very recent technology for language teachers and researchers and identify new opportunities for the creation of authentic learning experiences at both individual and sociocultural levels.