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Misato Hiraga – ProQuest LLC, 2024
This dissertation developed a new learner corpus of Japanese and introduced an error and linguistic annotation scheme specifically designed for Japanese particles. The corpus contains texts written by learners who are in the first year to fourth year university level Japanese courses. The texts in the corpus were tagged with part-of-speech and…
Descriptors: Japanese, Computational Linguistics, Form Classes (Languages), Error Analysis (Language)
Montri Tangpijaikul – LEARN Journal: Language Education and Acquisition Research Network, 2025
Despite the significant impact of the lexical approach for vocabulary learning, its classroom implementation has not been uniform. While related activities share the common Observe-Hypothesize-Experiment (OHE) elements, practitioners and researchers do not highlight how language input from the observing stage is turned into output and at what…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Teaching Methods
Atthasith Chuanpipatpong – PASAA: Journal of Language Teaching and Learning in Thailand, 2025
Writing is often considered the most difficult language skill for EFL learners due to its persistent grammatical and lexical challenges. Although tools such as Google Translate and ChatGPT are increasingly used, concerns persist regarding overreliance and reduced learner autonomy. This study investigated the grammatical errors and writing…
Descriptors: Foreign Countries, Error Analysis (Language), English (Second Language), Second Language Learning
Dongkawang Shin; Yuah V. Chon – Language Learning & Technology, 2023
Considering noticeable improvements in the accuracy of Google Translate recently, the aim of this study was to examine second language (L2) learners' ability to use post-editing (PE) strategies when applying AI tools such as the neural machine translator (MT) to solve their lexical and grammatical problems during L2 writing. This study examined 57…
Descriptors: Second Language Learning, Second Language Instruction, Translation, Computer Software
Ritonga, Mahyudin; Zulmuqim, Zulmuqim; Bambang, Bambang; Kurniawan, Rahadian; Pahri, Pahri – World Journal on Educational Technology: Current Issues, 2022
Information technology provides a lot of convenience for humans in completing their tasks and getting results according to targets. In line with that, language teachers have a duty to find out the level of language skills and forms of language errors in students. Machine Learning as part of technology can be maximized to detect forms of Arabic…
Descriptors: Arabic, Error Correction, Video Technology, Speech Communication
Arifi Waked; Muhammad W. Ashraf; Hanadi AbdelSalam; Khadija El Alaoui; Maura Pilotti – International Society for Technology, Education, and Science, 2024
Questions exist as to whether AI tools, such as ChatGPT, can aid learning. This study examined whether in-class exercises involving error detection in text generated by ChatGPT can aid students' foreign language writing. Participants were Arabic-English speakers who were classified as ranging from modest to competent English users according to…
Descriptors: Artificial Intelligence, Computer Software, Second Language Learning, Second Language Instruction
Chrysafiadi, Konstantina; Troussas, Christos; Virvou, Maria – International Journal of Learning Technology, 2022
This paper addresses the interesting issue of mobile-assisted language learning using novel techniques for further improving the adaptivity and personalisation to students. The domain model of the system includes English and French language concepts, and its user model holds information about students and their progress. It also embodies a…
Descriptors: Second Language Learning, Second Language Instruction, English (Second Language), French
Peer reviewedLabrie, Gilles; Singh, L. P. S. – CALICO Journal, 1991
The strategy used in "Miniprof," a program designed to provide "intelligent" instruction on elementary topics in French, is described. At an erroneous response, the program engages the student in a Socratic dialog and uses three major functions: parsing, error diagnostics, and tutoring. (10 references) (Author/LB)
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Error Analysis (Language), Error Correction
Peer reviewedJehle, Fred – CALICO Journal, 1987
SPANLAP (SPANish LAnguage Processing) is a computer program which attempts to engage students of Spanish in a free-form written dialog by allowing them to ask questions. The morphological and syntactic parsing process used in SPANLAP is explained. Sample dialog illustrates limitations of the current model. (Author/LMO)
Descriptors: Artificial Intelligence, College Students, Communicative Competence (Languages), Computer Assisted Instruction
Peer reviewedSanders, Alton; Sanders, Ruth – CALICO Journal, 1987
Describes the development in progress of a syntactic parser of German called "Syncheck," which uses the programing language "Prolog." The grammar is written in a formalism called "Definate Clause Grammar." The purpose of "Syncheck" is to provide advice on grammatical correctness to intermediate and advanced…
Descriptors: Artificial Intelligence, College Students, Courseware, Error Analysis (Language)

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