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Exploring AI-Driven Written English Assessment: Toward Improved Assessment Quality and Learner Outcomes.

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Title: Exploring AI-Driven Written English Assessment: Toward Improved Assessment Quality and Learner Outcomes.
Authors: Alshehri, Ahmed1, Aldossary, Saddah2, Jamshed, Mohammad3, Karim, Mohammad Rezaul3 karimrezaul318@gmail.com
Source: Journal of Language Teaching & Research. Mar2026, Vol. 17 Issue 2, p634-642. 9p.
Subject Terms: *Language ability testing, *Algorithmic bias, *Intelligent tutoring systems, *Effective teaching, *Ethical problems, Natural language processing
Abstract: The use of AI-powered tools for assessing written English is revolutionizing conventional evaluation methods by improving accuracy, consistency, and teaching efficiency. However, there is limited research on how AI assessment tools affect the validity and fairness of language evaluation. This review synthesizes current research on the technological foundations, pedagogical impact, and ethical considerations of AI in this area. This review assesses the role of AI in education, highlighting AI-driven technologies such as automated writing evaluation and natural language processing that provide timely feedback, support personalized learning, and reduce instructor workload. The study employed the PRISMA method to investigate the impact of AI on written English assessment by analyzing empirical studies from Scopus, Web of Science, and Google Scholar. The findings revealed that AI tools significantly enhanced the evaluation of written English by providing reliable assessments, immediate feedback, fostering learner autonomy, and producing assessments consistent with those of human raters, thereby promoting self-regulated writing and increasing access to quality feedback. However, specific concerns about algorithmic bias, feedback clarity, and the diminishing role of human judgment in assessing creativity and discourse were also found. It was also found that educational automation negatively impacts linguistic diversity, critical thinking, data privacy, transparency, and equitable access to AI tools. The review suggests integrating AI capabilities with human experience to balance technological efficiency and pedagogical integrity in the use of AI tools. It recommends developing hybrid assessment frameworks that integrate AI analytics with human evaluation to enhance fairness, accuracy, and comprehension in written English assessment. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Language Teaching & Research is the property of Academy Publication Co., LTD and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Language+Teaching+%26+Research%22">Journal of Language Teaching & Research</searchLink>. Mar2026, Vol. 17 Issue 2, p634-642. 9p.
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  Data: *<searchLink fieldCode="DE" term="%22Language+ability+testing%22">Language ability testing</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithmic+bias%22">Algorithmic bias</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Effective+teaching%22">Effective teaching</searchLink><br />*<searchLink fieldCode="DE" term="%22Ethical+problems%22">Ethical problems</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink>
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  Data: The use of AI-powered tools for assessing written English is revolutionizing conventional evaluation methods by improving accuracy, consistency, and teaching efficiency. However, there is limited research on how AI assessment tools affect the validity and fairness of language evaluation. This review synthesizes current research on the technological foundations, pedagogical impact, and ethical considerations of AI in this area. This review assesses the role of AI in education, highlighting AI-driven technologies such as automated writing evaluation and natural language processing that provide timely feedback, support personalized learning, and reduce instructor workload. The study employed the PRISMA method to investigate the impact of AI on written English assessment by analyzing empirical studies from Scopus, Web of Science, and Google Scholar. The findings revealed that AI tools significantly enhanced the evaluation of written English by providing reliable assessments, immediate feedback, fostering learner autonomy, and producing assessments consistent with those of human raters, thereby promoting self-regulated writing and increasing access to quality feedback. However, specific concerns about algorithmic bias, feedback clarity, and the diminishing role of human judgment in assessing creativity and discourse were also found. It was also found that educational automation negatively impacts linguistic diversity, critical thinking, data privacy, transparency, and equitable access to AI tools. The review suggests integrating AI capabilities with human experience to balance technological efficiency and pedagogical integrity in the use of AI tools. It recommends developing hybrid assessment frameworks that integrate AI analytics with human evaluation to enhance fairness, accuracy, and comprehension in written English assessment. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Language Teaching & Research is the property of Academy Publication Co., LTD and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2026
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