Generative AI Technology and Teacher Language Assessment Literacy – Can Language Teachers Keep up?
TL;DR
It is argued that teachers require prompting skills, critical evaluation of AI output, understanding of automated scoring systems, and pedagogical strategies to redesign assessments, and that these competences must be embedded in initial and in service teacher education.
Abstract
Language Assessment Literacy (LAL) has long been central to teachers’ professionalisation, yet the field has given relatively little attention to digitalisation. The rapid emergence of generative AI in language teaching and assessment now compels a reconceptualisation of teacher LAL to incorporate AI related knowledge, skills and ethical awareness. This paper maps key affordances—automated item generation, scalable scoring, and timely individualised feedback—alongside critical challenges including overreliance, academic integrity threats, bias, data protection and equity concerns. It argues that teachers require prompting skills, critical evaluation of AI output, understanding of automated scoring systems, and pedagogical strategies to redesign assessments, and that these competences must be embedded in initial and in service teacher education. Sustainable implementation demands coordinated action by policymakers, researchers, test developers, teacher educators and school leaders to provide localised frameworks, professional development, and time for practice. New conceptions of teacher LAL have to integrate a dynamic AI component, with AI literacy and related requirements representing a moving target and LAL levels being underdeveloped in many educational contexts.