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.

中文翻译：

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人工智能生成的第二语言可理解性反馈：一项关于效果和感知的探索性干预研究


本研究调查了人工智能 （AI） 模型和信号检测过程的使用，以对第二语言 （L2） 英语的可理解性产生有意义的视觉和类似 ChatGPT 的叙述反馈。为了测试此类技术的效果和感知，三组学习者 （N = 90） 在在线自定进度干预中接受了视觉和叙事反馈 （n = 30）、仅视觉反馈 （n = 29） 和无反馈 （n = 31），并明确指导了可理解性的分段和超分段特征。评分员评估脚本和自发语音中的口语前/口语后任务的可理解性、可理解性和口音，以及分段和超分段准确性。结果表明，视觉反馈提高了突出度的产生，但只有那些同时接受叙述（即 ChatGPT）反馈的参与者在三个韵律特征中的两个和可理解性方面有所改善。然而，那些收到叙述性反馈的人对练习活动有用性的看法最低。提供了对使用和改进基于 AI 的发音反馈的意义。