The effects of AI-guided individualized language learning: A meta-analysis
- Authors
- Lee, Hansol; Lee, Jang Ho
- Issue Date
- Jun-2024
- Publisher
- UNIV HAWAII, NATL FOREIGN LANGUAGE RESOURCE CENTER
- Keywords
- Artificial Intelligence; AI; Individualized Instruction; Language Learning; Meta-Analysis
- Citation
- LANGUAGE LEARNING & TECHNOLOGY, v.28, no.2, pp 134 - 162
- Pages
- 29
- Journal Title
- LANGUAGE LEARNING & TECHNOLOGY
- Volume
- 28
- Number
- 2
- Start Page
- 134
- End Page
- 162
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/75018
- ISSN
- 1094-3501
- Abstract
- Artificial intelligence (AI) has considerably advanced the methods for individualizing language learning opportunities, such as assessing learning progress and recommending effective individual instruction. In the present study, we conducted a meta -analysis to synthesize recent empirical findings pertaining to the utilization of AI -guided language learning and collected 61 samples (N = 8,282) from 17 research projects (e.g., Assessment to Instruction [A2i], Duolingo, and Project LISTEN). The results of our meta -analysis confirmed that AI -guided individualized language learning was effective for learners' language development (d = 1.18, based on 26 within -group samples, N = 2,262) and had an overall positive treatment effect compared to business -as -usual conditions (d = 0.39, based on 35 between -group samples, N = 6,020). Moreover, the results of our moderator analyses for the treatment effect revealed that AI -guided language learning with machine learning and hybrid systems were more impactful than those with rule -based systems, which may be more helpful (compared to the former) in understanding how predictions are made from a pedagogical perspective. Evidence -based implications are provided based on the results of this meta -analysis.
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