Integrating generative AI in English as an additional language or dialect education: Teacher prioritisation in curriculum support for indigenous and L2 learners

Abstract

This study examines how secondary English as an Additional Language or Dialect (EAL/D) teachers prioritise pedagogical, ethical, and contextual considerations when integrating Generative Artificial Intelligence (GenAI) into student support planning. Adopting a mixed-methods multiple-case study design, the study focuses on two teachers supporting EAL/D learners with distinct linguistic and cultural profiles (Indigenous; low-proficiency L2). Data were collected through Maximum Difference (MaxDiff) scaling tasks to elicit teachers’ prioritisation of key considerations, alongside a joint semi-structured interview, student support plans, and logs of GenAI prompts and outputs. The findings reveal that teachers consistently prioritised student-centred pedagogical goals and cultural responsiveness when developing their support plans, prior to any GenAI considerations. Analysis of prompt logs and interview data shows how these priorities were enacted in practice through GenAI, particularly via iterative prompt refinement and meta-prompting strategies. The data also highlight how teachers’ GenAI practices were shaped by contextual and institutional factors while they navigated ongoing tensions around their own knowledge limitations and the affordances and constraints of GenAI tools. The findings offer a unique, on-the-ground perspective on the practical and ethical integration of GenAI in pre-tertiary EAL/D education, providing actionable insights for educators to advance personalised learning through GenAI.