Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 4 |
| Since 2017 (last 10 years) | 4 |
| Since 2007 (last 20 years) | 4 |
Descriptor
Source
| Language Learning & Technology | 4 |
Author
| Godwin-Jones, Robert | 1 |
| Ji-young Shin | 1 |
| Mei-Rong Alice Chen | 1 |
| Ranalli, Jim | 1 |
| Yamashita, Taichi | 1 |
| Yujeong Choi | 1 |
Publication Type
| Journal Articles | 4 |
| Reports - Research | 3 |
| Tests/Questionnaires | 2 |
| Reports - Evaluative | 1 |
Education Level
| Higher Education | 3 |
| Postsecondary Education | 3 |
Audience
Location
| Canada | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Ji-young Shin; Yujeong Choi – Language Learning & Technology, 2025
The use of AI-powered chatbots has recently been extensively examined for second language (L2) learning. While their positive effects have been widely reported regarding L2 English learning, studies involving less commonly taught languages (LCTLs) are scant. The current study incorporated an AI chatbot called Iruda in L2 Korean teaching, to…
Descriptors: Artificial Intelligence, Computer Software, Synchronous Communication, Korean
Mei-Rong Alice Chen – Language Learning & Technology, 2024
This study explores the impact of an innovative approach that combines artificial intelligence (AI) chatbot support with collaborative note-taking (CNT) in the comprehension of semantic terms among English as a Foreign Language (EFL) learners. Given the significance of semantics in English language learning, traditional didactic methods often…
Descriptors: Artificial Intelligence, Computer Software, Synchronous Communication, Semantics
Godwin-Jones, Robert – Language Learning & Technology, 2022
In recent years, advances in artificial intelligence (AI) have led to significantly improved, or in some cases, completely new digital tools for writing. Systems for writing assessment and assistance based on automated writing evaluation (AWE) have been available for some time. That is the case for machine translation as well. More recent are…
Descriptors: Writing Instruction, Artificial Intelligence, Feedback (Response), Writing Evaluation
Ranalli, Jim; Yamashita, Taichi – Language Learning & Technology, 2022
To the extent automated written corrective feedback (AWCF) tools such as Grammarly are based on sophisticated error-correction technologies, such as machine-learning techniques, they have the potential to find and correct more common L2 error types than simpler spelling and grammar checkers such as the one included in Microsoft Word (technically…
Descriptors: Error Correction, Feedback (Response), Computer Software, Second Language Learning

Peer reviewed
Direct link
