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Artificial Intelligence Knowledge Development

<p style="text-align: justify;">The core principles of validity, reliability, and fairness have been the foundation of ethical assessment practices, as discussed in classical validation theories (e.g., Chapelle et al., 2008; Kane, 1992, 2013) and the American Educational Research Association (AERA), the American Psychological Association (APA), and the National Council on Measurement in Education Standards (NCME) (AERA &amp; APA &amp; NCME, 2014). The Standards have provided best practices for AI use in high-stakes testing, particularly in the automated scoring of written and spoken responses. Responsible AI (RAI) is essential across industry domains, including educational assessments. With recent advancements in generative AI, new policies and guidance on applying RAI principles in assessment have emerged. Expanding on Chapelle et al.'s (2008) work, this paper introduces a unified assessment framework that integrates traditional validation theory with both assessment-specific and domain-agnostic RAI principles. This framework supports responsible AI use, aligns with ethical principles to uphold human values and oversight, and promotes broader social responsibility in AI-driven assessments.</p>

ltrq.2025.50.09.pdf

Perspective Article

ltrq.2025.50.09.xml

Where Assessment Validation and Responsible AI Meet

Guest-editors: 
Christine Coombe 
Higher Colleges of Technology, Dubai, UAE 
Tony Clark 
Cambridge University Press & Assessment, UK 
Hassan Mohebbi 
European Knowledge Development Institute, Turkey


Special Issue: In Honour of Carol A. Chapelle’s Contributions to Language Assessment and Learning (Volume 50-First Part)