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Dong-Ok Won; Yu Kyoung Shin; Ho-Jung Kim; Isaiah WonHo Yoo – Language Assessment Quarterly, 2025
Despite the growing interest in AI for language assessment, there remains a significant research gap regarding its usefulness for assessing less proficient language skills, particularly those of learners of English as a second or foreign language (S/FL). AI models often prioritize proficient writing, neglecting the intricacies of learner language.…
Descriptors: Artificial Intelligence, Computer Software, Phrase Structure, Native Language
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Hanne Roothooft; Amparo Lázaro-Ibarrola; Bram Bulté – Language Teaching Research, 2025
Second language (L2) writing research has demonstrated that young learners discuss linguistic issues, make use of feedback, and show a generally positive disposition toward writing tasks. However, many issues deserve further investigation. Regarding task implementation, few studies have been conducted with young learners writing individually, and…
Descriptors: Error Correction, Feedback (Response), Accuracy, Writing Instruction
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Dillon, Thomas; Wells, Donald – English Teaching, 2023
This study examined effects of pronunciation training using automatic speech recognition technology on common pronunciation errors of Korean English learners. Participants were divided into two groups. One group was given instruction and training about the use of automatic speech recognition for pronunciation practice. The other group was not…
Descriptors: Pronunciation, English (Second Language), Second Language Instruction, English Language Learners
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Fabian Kieser; Peter Wulff; Jochen Kuhn; Stefan Küchemann – Physical Review Physics Education Research, 2023
Generative AI technologies such as large language models show novel potential to enhance educational research. For example, generative large language models were shown to be capable of solving quantitative reasoning tasks in physics and concept tests such as the Force Concept Inventory (FCI). Given the importance of such concept inventories for…
Descriptors: Physics, Science Instruction, Artificial Intelligence, Computer Software
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Christopher Saarna – International Journal of Technology in Education, 2024
This study seeks to clarify whether teachers are able to distinguish between essays written by English L2 students or generated by ChatGPT. 47 instructors who hold experience teaching English to native speakers of Japanese in universities or other higher education institutions were tested on whether they could identify between human written essays…
Descriptors: Identification, Artificial Intelligence, Computer Software, Grammar
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Dahlkemper, Merten Nikolay; Lahme, Simon Zacharias; Klein, Pascal – Physical Review Physics Education Research, 2023
This study aimed at evaluating how students perceive the linguistic quality and scientific accuracy of ChatGPT responses to physics comprehension questions. A total of 102 first- and second-year physics students were confronted with three questions of progressing difficulty from introductory mechanics (rolling motion, waves, and fluid dynamics).…
Descriptors: Physics, Science Instruction, Artificial Intelligence, Computer Software
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Loboda, Krzysztof; Mastela, Olga – Interpreter and Translator Trainer, 2023
Mass adoption of neural machine translation (NMT) tools in the translation workflow has exerted a significant impact on the language services industry over the last decade. There are claims that with the advent of NMT, automated translation has reached human parity for translating news (see, e.g. Popel et al. 2020). Moreover, some machine…
Descriptors: Computer Software, Computational Linguistics, Polish, Folk Culture
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Burhan Ozfidan; Dina Abdel Salam El-Dakhs; Lama Adel Alsalim – Contemporary Educational Technology, 2024
This study explores Saudi undergraduate students' perceptions of artificial intelligence (AI) tools in academic writing. Despite extensive research on AI in higher education, there is limited focus on academic writing, especially in the Saudi context. A survey of 189 students, proficient in English and enrolled in freshmen academic writing…
Descriptors: Foreign Countries, Artificial Intelligence, Technology Uses in Education, Grammar
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Xu, Wenwen; Kim, Ji-Hyun – English Teaching, 2023
This study explored the role of written languaging (WL) in response to automated written corrective feedback (AWCF) in L2 accuracy improvement in English classrooms at a university in China. A total of 254 freshmen enrolled in intermediate composition classes participated, and they wrote 4 essays and received AWCF. A half of them engaged in WL…
Descriptors: Grammar, Accuracy, Writing Instruction, Writing Evaluation
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Xu, Yi – Interpreter and Translator Trainer, 2023
The research on interpreting aptitude has focused on the abilities, skills and personal traits of individuals in order to predict their future interpreting performance. However, an important variable between the personal characteristics and success of trainee interpreters in interpreter training, which is instructional practices, is overlooked.…
Descriptors: Prediction, Language Aptitude, Feedback (Response), Short Term Memory
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Ahmed Zarour; Samer Zein – International Journal of Technology in Education and Science, 2019
Software Effort Estimation is one of the most challenging aspects in the software development life cycle. Recent empirical studies in the area of software development estimation indicate the presence of two models for effort estimation: (i) Formal, and (ii) Expert Based (Informal). The IT sector in Palestine is one of the most promising and…
Descriptors: Computer Software, Case Studies, Information Technology, Teamwork
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Liyanagunawardena, Tharindu R. – European Journal of Open, Distance and E-Learning, 2020
Transcripts and captions make videos more accessible to everyone. However, the time and resources required for manual transcription are a known barrier in creating accessible videos. This paper presents a small study where students (283) and tutors (27) reported their views on automatic transcriptions for recorded webinar videos. Despite not…
Descriptors: Transcripts (Written Records), Video Technology, Assistive Technology, Students with Disabilities
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Awadh, Awadh Nasser Munassar; Khan, Ansarullah Shafiull – Journal of Language and Linguistic Studies, 2020
This study aims at investigating the challenges that Yemeni translation students encounter when translating neologisms from English into Arabic. It also aims at comparing students' translation with outcomes of machine translation (MT). The authors follow the descriptive and comparative methods in conducting this study. To achieve the objective of…
Descriptors: Barriers, Translation, English (Second Language), Semitic Languages
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Shi, Zhan; Liu, Fengkai; Lai, Chun; Jin, Tan – Language Learning & Technology, 2022
Automated Writing Evaluation (AWE) systems have been found to enhance the accuracy, readability, and cohesion of writing responses (Stevenson & Phakiti, 2019). Previous research indicates that individual learners may have difficulty utilizing content-based AWE feedback and collaborative processing of feedback might help to cope with this…
Descriptors: Writing Instruction, Writing Evaluation, Feedback (Response), Accuracy
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Daniels, Paul; Iwago, Koji – JALT CALL Journal, 2017
As online automatic speech recognition (ASR) engines become more accurate and more widely implemented with call software, it becomes important to evaluate the effectiveness and the accuracy of these recognition engines using authentic speech samples. This study investigates two of the most prominent cloud-based speech recognition engines--Apple's…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Computer Software