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It first surveys current research on AS, then explains how automated scoring shapes present and future assessment, teaching, and learning. The review outlines AS background in language assessment and testing, details the literature search process and inclusion criteria, and organizes findings into three themes: design considerations, the roles of humans and AI, and accuracy across different groups. 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Gregory Tweedie, University of Calgary  \nAbstract  \nThis article reviews recent literature (2011–present) on the automated scoring (AS) of writing and speaking. Its purpose is to first survey the current research on automated scoring of language, then highlight how automated scoring impacts the present and future of assessment, teaching, and learning.  \nThe article begins by outlining the general background of AS issues in language assessment and testing. It then positions AS research with respect to technological advancements. Section two details the literature review search process and criteria for article inclusion. In section three, the three main themes emerging from the review are presented: automated scoring design considerations, the role of humans and artificial intelligence, and the accuracy of automated scoring with different groups. Two tables show how specific articles contributed to each of the themes. Following this, each of the three themes is presented in further detail, with a sequential focus on writing, speaking, and a short summary. Section four addresses AS implementation with respect to current assessment, teaching, and learning issues. Section five considers future research possibilities related to both the research and current uses of AS, with implications for the Canadian context in terms of the next steps for automated scoring. Keywords: Automated scoring of language ; Literature review ; Scoring feedback; Technology in language assessment and teaching  \nRésumé  \nCet article examine la littérature récente (2011 jusqu’à présent) sur la notation automatisée (NA) de l'expression écrite et de l’expression orale. Son objectif est d'abord d'examiner les recherches actuelles sur la notation automatisée de la langue, puis de mettre en évidence l'impact de la notation automatisée sur le présent et l'avenir de l'évaluation, de l'enseignement et de l'apprentissage. L'article commence par décrire le contexte général des problèmes de notation automatisée dans l'évaluation et  \nCJLT/RCAT Vol. 48 (3)  \nles tests linguistiques. Il positionne ensuite la recherche sur la NA par rapport aux avancées technologiques. La deuxième section décrit en détail le processus de recherche de la revue de la littérature et les critères d'inclusion des articles. Dans la troisième section, les trois principaux thèmes qui se dégagent de l’analyse sont présentés : considérations relatives à la conception de la notation automatisée; le rôle des humains et de l'intelligence artificielle; et la précision de la notation automatisée avec différents groupes. Deux tableaux montrent comment des articles spécifiques ont contribué à chacun des thèmes. Ensuite, chacun des trois thèmes est présenté plus en détail, avec un accent séquentiel sur l'expression écrite, l’expression orale et un brefrésumé . La quatrième section aborde la mise en œuvre des NA en ce qui concerne les questions actuelles d'évaluation,  \nd'enseignement et d'apprentissage. La cinquième section présente les possibilités de recherche futures liées à la recherche et aux utilisations actuelles de la NA, avec des implications sur le contexte canadien en ce qui concerne les prochaines étapes de la NA.  \nMots-clés : Notation automatisée de la langue ; revue de littérature ; rétroaction sur la notation ; technologie dans l’évaluation et enseignement des langues  \nBackground on Automated Scoring  \nAutomated scoring (AS) has been the focus of ongoing academic research and development since the 1960s and has enjoyed increasing attention alongside technological advances (Foltz et al., 2020) . In the context of language assessment, AS can be defined as “…using computers to convert students’ performance on educa","cbCaiuWOWLYXPFAa","https://ap.wps.com/l/cbCaiuWOWLYXPFAa","pdf",150763,22,"English","# Abstract\n# Background on Automated Scoring\n## Definition and rationale\n## Goals for language assessment, teaching, and learning\n# Literature Review Process\n## Search process and inclusion criteria\n# Emerging Themes\n## Automated scoring design considerations\n## Role of humans and artificial intelligence\n## Accuracy across different groups\n# Automated Scoring Implementation\n## Current assessment, teaching, and learning issues\n# Future Research and Canadian Implications","[{\"question\":\"What does automated scoring mean in language assessment?\",\"answer\":\"Automated scoring uses computers to convert students’ performance on educational tasks into characterizations of performance quality. It is defined in the article as turning language assessment responses into evaluative characterizations of quality.\"},{\"question\":\"Why has automated scoring received increasing attention?\",\"answer\":\"The article notes motivations such as cost reduction, scalability, consistent and accurate assessment, and the ability to provide immediate feedback or results. It also links growing interest to advances in computing, NLP, and machine learning.\"},{\"question\":\"What major themes does the literature review identify?\",\"answer\":\"The review presents three themes: automated scoring design considerations, the role of humans and artificial intelligence, and the accuracy of automated scoring with different groups. It further connects specific articles to each theme through tables and expanded discussion.\"}]","Volume 48 - Issue 3 - Automated Scoring of Speaking and Writing: Starting to Hit its Stride | PDF",55]