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ORCID

Okan Bulut: https://orcid.org/0000-0001-5853-1267

Yi Zheng: https://orcid.org/0000-0003-2671-0820

Abstract

This editorial introduces the third and final installment of the \textit{Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement}. Building on the first two parts, which explored AI-driven innovations in assessment design, scoring, learning analytics and their ethical challenges, this issue features two papers that offer timely and extensive insights into the integration of natural language processing (NLP) and generative language models in educational measurement --- a tutorial on building an NLP pipeline in R for analyzing constructed-response data, and a systematic review of generative language models for automated writing evaluation. With this third installment, we bring to a close the \textit{Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement}. Across all three parts, the collected contributions have demonstrated both the remarkable promise and the profound challenges that accompany the infusion of AI and ML into assessment research and practice. As AI continues to transform our field rapidly, we hope this special issue will inspire researchers, practitioners, and policymakers to work toward a better future in which AI-powered assessment practices are technologically advanced, ethically sound, and fully supportive of diverse learners.

DOI

https://doi.org/10.59863/DJYM9770

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