Mathematics disciplinary literacy: a case study of a bilingual teacher’s interaction with ChatGPT

This is a qualitative case study of one elementary bilingual teacher’s instructional planning via a STEM-disciplinary literacy approach while interacting with ChatGPT 3.5, a large language model. Informed by teacher professional knowledge models and framed by the theory of language, Systemic Functional Linguistics, we investigate how one bilingual teacher reasons pedagogically for mathematics disciplinary literacy and how she integrates generative artificial intelligence (GenAI) into her lesson planning for emergent bilinguals. Data sources include a mathematics lesson plan, a teacher log of prompts (input) and output of interactions with ChatGPT 3.5, and teacher reflections. A thematic analysis is conducted to reveal three themes: (1) iterative prompt engineering for vocabulary, (2) iterative prompt engineering for syntax and activities, and (3) affordances and challenges in teacher-led prompt engineering for mathematics disciplinary literacy. Results demonstrate a bilingual teachers’ integration of content and language pedagogical planning while using GenAI as an instructional planning tool for a 1st grade mathematics lesson for emergent bilinguals. Implications suggest a need to further explore the integration of GenAI into teachers’ professional knowledge for teaching emergent bilinguals, and a need for teacher professional development for AI literacy in bilingual teachers of STEM education.