Artificial Intelligence‐Generated Feedback for Second Language Intelligibility: An Exploratory Intervention Study on Effects and Perceptions

Abstract This study investigated the use of Artificial Intelligence (AI) models and signal detection processes to generate meaningful visual and ChatGPT‐like narrative feedback on second language (L2) English intelligibility. To test the effects and perceptions of such techniques, three groups of learners ( N = 90) received visual and narrative feedback ( n = 30), visual‐only feedback ( n = 29), and no feedback ( n = 31) in an online self‐paced intervention with explicit instruction on segmental and suprasegmental features of intelligibility. Pre/postspeaking tasks were evaluated by raters for intelligibility, comprehensibility, and accentedness, as well as segmental and suprasegmental accuracy, in scripted and spontaneous speech. The results indicate that visual feedback improves prominence production, but only those participants who also received the narrative (i.e., ChatGPT) feedback improved in two of the three prosodic features and in intelligibility. However, those who received narrative feedback had the lowest perceptions of the practice activity helpfulness. Implications for the use and improvement of AI‐based pronunciation feedback are provided.

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Publication Details

Journal
Language Learning
Published
2025-05-15
DOI
https://doi.org/10.1111/lang.12719
Citations
4
Primary Topic
Second Language Acquisition and Learning
Type
article
Field-Weighted Citation Impact
13.78

Funders

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article

Artificial Intelligence‐Generated Feedback for Second Language Intelligibility: An Exploratory Intervention Study on Effects and Perceptions

Okim Kang, Kevin Hirschi, John H. L. Hansen, Yang Mu et al.
4 citations
Language Learning
Second Language Acquisition and Learning
13.78
article

Artificial Intelligence‐Generated Feedback for Second Language Intelligibility: An Exploratory Intervention Study on Effects and Perceptions

Okim Kang, Kevin Hirschi, John H. L. Hansen, Yang Mu, Kyle Beloin
article en
4 citations

Abstract

Abstract This study investigated the use of Artificial Intelligence (AI) models and signal detection processes to generate meaningful visual and ChatGPT‐like narrative feedback on second language (L2) English intelligibility. To test the effects and perceptions of such techniques, three groups of learners ( N = 90) received visual and narrative feedback ( n = 30), visual‐only feedback ( n = 29), and no feedback ( n = 31) in an online self‐paced intervention with explicit instruction on segmental and suprasegmental features of intelligibility. Pre/postspeaking tasks were evaluated by raters for intelligibility, comprehensibility, and accentedness, as well as segmental and suprasegmental accuracy, in scripted and spontaneous speech. The results indicate that visual feedback improves prominence production, but only those participants who also received the narrative (i.e., ChatGPT) feedback improved in two of the three prosodic features and in intelligibility. However, those who received narrative feedback had the lowest perceptions of the practice activity helpfulness. Implications for the use and improvement of AI‐based pronunciation feedback are provided.

Language LearningVol. 75(S1)
The University of Texas at Dallas (US), Northern Arizona University (US), Film Independent (US), The University of Texas at San Antonio (US)
National Science Foundation
Openalex Percentile: Top 1%
Second Language Acquisition and Learning
13.78
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Artificial Intelligence‐Generated Feedback for Second Language Intelligibility: An Exploratory Intervention Study on Effects and Perceptions — Okim Kang, Kevin Hirschi, et al. · Language Learning (2025) | TGRS Research Map | TGRS