Rethinking Ethics, Standards, and Criteria in Language Testing and Assessment in the Age of AI

Dear Colleagues,

The rapid integration of artificial intelligence (AI) into language education is evident. It is reshaping teaching and learning styles, as well as language testing and assessment practices. While AI presents new opportunities for personalization, efficiency, scalability, and innovation in assessment, it challenges the long‑standing principles in language testing, giving way to ethical, methodological, and epistemological concerns. Such concerns relate to algorithmic bias, data privacy, construct shift, automated decisions, automated feedback, automated scoring, and the evolving role of human evaluation and assessment. These concerns have prompted renewed scrutiny of standards and ethical guidelines. There is a growing need for critical, empirical, and conceptual work that revisits ethical frameworks, quality criteria, and standards in language assessment to ensure they remain fit for purpose in the age of AI and rethink assessment literacy for teachers and learners.

This Special Issue is situated within the most recent advances in research, theory, and practice related to ethics, standards, and criteria in language testing and assessment in AI‑mediated contexts. It seeks to bring together interdisciplinary perspectives from applied linguistics, language testing, educational measurement, AI ethics, and educational technology. Considering the ongoing concerns about the reliability and fairness of AI-mediated assessment, as well as arguments that AI may signal the end of traditional testing, we aim to contribute to these debates by examining the potential for transformative assessment with AI. We address the question of which aspects of assessment and evaluation should remain the responsibility of teachers when AI systems can assess students’ assignments and examinations.

Submissions may cover, but are not limited to, the following:

• Validity, reliability, and fairness in automated and AI‑assisted scoring.
• Algorithmic bias, equity, and inclusivity in language assessment systems.
• Transparency, explainability, and accountability of AI‑driven assessment decisions.
• Ethical frameworks for AI‑mediated language testing.
• Assessment of out-of-class language learning.
• Construct definition and construct shift in the presence of generative AI tools.
• Fostering human-AI approaches to assessment design, scoring, and feedback.
• Data privacy, surveillance, and governance in large‑scale AI‑based assessments.
• AI, academic integrity, and the ethics of test use and interpretation.
• Assessment literacy for teachers, testers, and learners in AI‑rich environments.
• Policy implications and regulatory responses to AI in language testing.
• Empirical studies, theoretical analyses, methodological innovations, and practitioner‑oriented perspectives related to AI and language assessment ethics.

The submissions can be empirical research articles, conceptual papers, methodological contributions, and practice‑oriented studies that advance scholarly understanding and inform responsible assessment practice in this rapidly evolving domain.

Dr. Abdu Al-Kadi
Dr. Jamal Kaid Mohammed Ali
Dr. Asma Maaoui
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.