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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
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
Mughaz, Dror; Cohen, Michael; Mejahez, Sagit; Ades, Tal; Bouhnik, Dan – Interdisciplinary Journal of e-Skills and Lifelong Learning, 2020
Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student's grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence…
Descriptors: Artificial Intelligence, Teaching Methods, Brain Hemisphere Functions, Grammar
What to Expect from Neural Machine Translation: A Practical In-Class Translation Evaluation Exercise
Moorkens, Joss – Interpreter and Translator Trainer, 2018
Machine translation is currently undergoing a paradigm shift from statistical to neural network models. Neural machine translation (NMT) is difficult to conceptualise for translation students, especially without context. This article describes a short in-class evaluation exercise to compare statistical and neural MT, including details of student…
Descriptors: Translation, Teaching Methods, Computational Linguistics, Quality Assurance
Peer reviewedRuschoff, Bernd – System, 1986
Argues that sufficient adaptivity in computer-assisted language learning materials for individual study can be achieved only if such programs are able to perform meaningful error analysis and fit the needs of individual learners. Other effective elements of the program include collecting information on learning and performance histories of…
Descriptors: Artificial Intelligence, Autoinstructional Aids, Computer Assisted Instruction, Dialogs (Language)
Cerri, Stefano; Breuker, Joost – Studies in Language Learning, 1981
Characteristics of DART (Didactic Augmented Recursive Transition), an ATN-based system for writing intelligent computer assisted instruction (ICAI) programs that is available on the PLATO system are described. DART allows writing programs in an ATN dialect, compiling them in machine code for the PLATO system, and executing them as if the original…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Educational Diagnosis, Error Analysis (Language)

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