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Journal : IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature

Pre-Service Language Teachers’ Perceptions of AI-Driven Language Assessment: A Preliminary Investigation Sidqi, M. Fajru; Matra, Sarlita; Ulya, Inayatul
IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature Vol. 13 No. 1 (2025): IDEAS: Journal on English Language Teaching and Learning, Linguistics and Lite
Publisher : Institut Agama Islam Negeri Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/ideas.v13i1.6816

Abstract

The emergence of artificial intelligence (AI) in education has begun reshaping language assessment practices, demanding that future educators develop not only assessment literacy but also a critical understanding of AI-driven tools. This preliminary study explores the perceptions and beliefs of pre-service language teachers in Indonesia regarding AI-based language assessment, with a focus on how these perceptions influence their readiness to navigate the evolving landscape of educational evaluation. Employing a mixed-methods approach through open-ended surveys, the study investigates participants’ views on the pedagogical potential of AI technologies in supporting formative and summative assessment, as well as the challenges they face in adopting such innovations. Findings indicate that while many pre-service teachers acknowledge the efficiency and objectivity offered by AI, they express uncertainty about its reliability, ethical implications, and their own preparedness to effectively integrate AI tools into classroom assessment. These insights point to an urgent need for teacher education programs to incorporate AI-related assessment training that fosters not only technical skills but also reflective and ethical awareness. This study contributes to the growing discourse on AI in language education, advocating for a reimagined teacher preparation model that equips future educators with the competencies needed for responsible and effective use of AI-driven assessment systems.