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Ikkyu Choi; Jiangang Hao; Chen Li; Michael Fauss; Jakub Novák – ETS Research Report Series, 2024
A frequently encountered security issue in writing tests is nonauthentic text submission: Test takers submit texts that are not their own but rather are copies of texts prepared by someone else. In this report, we propose AutoESD, a human-in-the-loop and automated system to detect nonauthentic texts for a large-scale writing tests, and report its…
Descriptors: Writing Tests, Automation, Cheating, Plagiarism
Zhao, Ruibin; Zhuang, Yipeng; Zou, Di; Xie, Qin; Yu, Philip L. H. – Education and Information Technologies, 2023
Grading assignments is inherently subjective and time-consuming; automatic scoring tools can greatly reduce teacher workload and shorten the time needed for providing feedback to learners. The purpose of this paper is to propose a novel method for automatically scoring student responses to picture-cued writing tasks. As a popular paradigm for…
Descriptors: Artificial Intelligence, Automation, Scoring, Visual Aids
Dongkwang Shin; Jang Ho Lee – ELT Journal, 2024
Although automated item generation has gained a considerable amount of attention in a variety of fields, it is still a relatively new technology in ELT contexts. Therefore, the present article aims to provide an accessible introduction to this powerful resource for language teachers based on a review of the available research. Particularly, it…
Descriptors: Language Tests, Artificial Intelligence, Test Items, Automation
Han, Chao – Language Testing, 2022
Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the…
Descriptors: Translation, Language Tests, Testing, Evaluation Methods
Xinming Chen; Ziqian Zhou; Malila Prado – International Journal of Assessment Tools in Education, 2025
This study explores the efficacy of ChatGPT-3.5, an AI chatbot, used as an Automatic Essay Scoring (AES) system and feedback provider for IELTS essay preparation. It investigates the alignment between scores given by ChatGPT-3.5 and those assigned by official IELTS examiners to establish its reliability as an AES. It also identifies the strategies…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Automation
Liu, Houjun; MacWhinney, Brian; Fromm, Davida; Lanzi, Alyssa – Journal of Speech, Language, and Hearing Research, 2023
Purpose: A major barrier to the wider use of language sample analysis (LSA) is the fact that transcription is very time intensive. Methods that can reduce the required time and effort could help in promoting the use of LSA for clinical practice and research. Method: This article describes an automated pipeline, called Batchalign, that takes raw…
Descriptors: Automation, Language Tests, Computational Linguistics, Morphology (Languages)
Ockey, Gary J.; Neiriz, Reza – Assessment in Education: Principles, Policy & Practice, 2021
As our understanding of the construct of oral communication (OC) has evolved, so have the possibilities of computer technology undertaking the delivery of tests that measure this ability. It is paramount to understand to what extent such developments lead to accurate, comprehensive, and useful assessment of OC. In this paper, we discuss five…
Descriptors: Speech Communication, Computer Assisted Testing, Speech Tests, English (Second Language)
Kornwipa Poonpon; Paiboon Manorom; Wirapong Chansanam – Contemporary Educational Technology, 2023
Automated essay scoring (AES) has become a valuable tool in educational settings, providing efficient and objective evaluations of student essays. However, the majority of AES systems have primarily focused on native English speakers, leaving a critical gap in the evaluation of non-native speakers' writing skills. This research addresses this gap…
Descriptors: Automation, Essays, Scoring, English (Second Language)
Advancing Language Assessment with AI and ML--Leaning into AI Is Inevitable, but Can Theory Keep Up?
Xiaoming Xi – Language Assessment Quarterly, 2023
Following the burgeoning growth of artificial intelligence (AI) and machine learning (ML) applications in language assessment in recent years, the meteoric rise of ChatGPT and its sweeping applications in almost every sector have left us in awe, scrambling to catch up by developing theories and best practices. This special issue features studies…
Descriptors: Artificial Intelligence, Theory Practice Relationship, Language Tests, Man Machine Systems
Richardson, Mary; Clesham, Rose – London Review of Education, 2021
Our world has been transformed by technologies incorporating artificial intelligence (AI) within mass communication, employment, entertainment and many other aspects of our daily lives. However, within the domain of education, it seems that our ways of working and, particularly, assessing have hardly changed at all. We continue to prize…
Descriptors: Artificial Intelligence, High Stakes Tests, Computer Assisted Testing, Educational Change
Charles Hulme; Joshua McGrane; Mihaela Duta; Gillian West; Denise Cripps; Abhishek Dasgupta; Sarah Hearne; Rachel Gardner; Margaret Snowling – Language, Speech, and Hearing Services in Schools, 2024
Purpose: Oral language skills provide a critical foundation for formal education and especially for the development of children's literacy (reading and spelling) skills. It is therefore important for teachers to be able to assess children's language skills, especially if they are concerned about their learning. We report the development and…
Descriptors: Automation, Language Tests, Standardized Tests, Test Construction
de Jong, Nivja H.; Pacilly, Jos; Heeren, Willemijn – Assessment in Education: Principles, Policy & Practice, 2021
Fluency in terms of speed of speech and (lack of) hesitations such as silent and filled pauses ('uhm's) is part of oral proficiency. Language assessment rubrics therefore include aspects of fluency. Measuring fluency, however, is highly time-consuming because of the manual labour involved. The current paper aims to automatically measure aspects of…
Descriptors: Language Fluency, Speech Skills, Second Languages, Indo European Languages
Gordon Matthew – Technology, Pedagogy and Education, 2025
In most universities in South Africa, English is still preferred as the medium of instruction. However, most schools in South Africa have adopted a home-language education approach. Those school students then have a lower English proficiency compared to other students from English schools. Providing these students with instructional assistance…
Descriptors: Eye Movements, Captions, English (Second Language), Second Language Learning
Davis, Larry; Papageorgiou, Spiros – Assessment in Education: Principles, Policy & Practice, 2021
Human raters and machine scoring systems potentially have complementary strengths in evaluating language ability; specifically, it has been suggested that automated systems might be used to make consistent measurements of specific linguistic phenomena, whilst humans evaluate more global aspects of performance. We report on an empirical study that…
Descriptors: Scoring, English for Academic Purposes, Oral English, Speech Tests
Gong, Kaixuan – Asian-Pacific Journal of Second and Foreign Language Education, 2023
The extensive use of automated speech scoring in large-scale speaking assessment can be revolutionary not only to test design and rating, but also to the learning and instruction of speaking based on how students and teachers perceive and react to this technology. However, its washback remained underexplored. This mixed-method study aimed to…
Descriptors: Second Language Learning, Language Tests, English (Second Language), Automation