Supporting Iranian Teachers’ Language Assessment Literacy: A Hybrid AI–Human Feedback Approach within Collaborative Action Research

This study explores the influence of a hybrid AI-human feedback model, incorporated within a Collaborative Action Research (CAR) framework, on developing Language Assessment Literacy (LAL) among Iranian English language teachers. Grounded in the challenges of conventional assessment practices in Iran’s EFL context, containing overreliance on summative testing and inadequate professional development, the research engaged a qualitative CAR methodology with 15 teachers over six months. Participants involved in iterative cycles of assessment design, application, and reflection, supported by AI-driven analytics (e.g., automated scoring and natural language processing (NLP) feedback) alongside structured peer and mentor collaboration. Findings indicated that the AI-CAR synergy improved teachers’ diagnostic precision, empowered culturally responsive assessment adaptations, and nurtured sustainable shifts toward formative practices. Main results involved improved recognition of learner patterns, critical negotiation of algorithmic feedback, and the emergence of teacher-led assessment innovations. Yet, challenges appeared concerning workload intensity and contextualizing AI recommendations within Iranian instructional and cultural norms. This study addressed the research problem of underdeveloped LAL among Iranian EFL school teachers, considered by overreliance on summative testing. Implementing a qualitative CAR design with 15 teachers over six months, it explored how a hybrid AI-human feedback model influences LAL development. Key findings indicate the model improved teachers’ diagnostic precision and nurtured formative assessment practices through critical negotiation of AI feedback. The conclusion underlines that sustainable LAL development requires hybrid models where AI augments, not substitutes, teacher judgment.