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<p>This study investigates the effects of Foreign Language Intelligent Teaching (FLIT)-integrated blended learning (FBL) on Business English proficiency and student engagement in a Chinese university context. Using a quasi-experimental design with 472 undergraduates, the research compares an experimental group (<i>n</i> = 240) exposed to AI-driven blended instruction with a control group (<i>n</i> = 232) taught via conventional methods. ANCOVA results indicated significantly higher post-test scores in the experimental group across reading (<i>F</i>(1, 466) = 26.90, <i>p</i> &lt; .001, <i>η</i><sup>2</sup><i>ₚ</i> = .055), listening (<i>F</i>(1, 466) = 36.20, <i>p</i> &lt; .001, <i>η</i><sup>2</sup><i>ₚ</i> = .072), writing (<i>F</i>(1, 466) = 47.70, <i>p</i> &lt; .001, <i>η</i><sup>2</sup><i>ₚ</i> = .093), and speaking (<i>F</i>(1, 466) = 34.49, <i>p</i> &lt; .001, <i>η</i><sup>2</sup><i>ₚ</i> = .069). Independent samples <i>t</i>-tests revealed significantly greater cognitive (<i>t</i>(470) = 3.73, <i>p</i> &lt; .001, <i>d</i> = .344) and behavioral engagement (<i>t</i>(470) = 7.09, <i>p</i> &lt; .001, <i>d</i> = .653) in the experimental group, while emotional engagement showed a marginal difference (Welch’s <i>t</i>(426) = − 1.95, <i>p</i> = .051, <i>d</i> = − .18). These findings provide robust empirical support for the pedagogical effectiveness of AI-enhanced instruction in improving Business English outcomes and learner engagement. This study explores innovative methods in language assessment and technology-enhanced language teaching by providing empirical validation of an AI-driven learning and assessment platform for Business English instruction.</p>

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An empirical study of the AI-driven platform in blended learning for Business English performance and student engagement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Sha Cao,
  • Satha Phongsatha

摘要

This study investigates the effects of Foreign Language Intelligent Teaching (FLIT)-integrated blended learning (FBL) on Business English proficiency and student engagement in a Chinese university context. Using a quasi-experimental design with 472 undergraduates, the research compares an experimental group (n = 240) exposed to AI-driven blended instruction with a control group (n = 232) taught via conventional methods. ANCOVA results indicated significantly higher post-test scores in the experimental group across reading (F(1, 466) = 26.90, p < .001, η2ₚ = .055), listening (F(1, 466) = 36.20, p < .001, η2ₚ = .072), writing (F(1, 466) = 47.70, p < .001, η2ₚ = .093), and speaking (F(1, 466) = 34.49, p < .001, η2ₚ = .069). Independent samples t-tests revealed significantly greater cognitive (t(470) = 3.73, p < .001, d = .344) and behavioral engagement (t(470) = 7.09, p < .001, d = .653) in the experimental group, while emotional engagement showed a marginal difference (Welch’s t(426) = − 1.95, p = .051, d = − .18). These findings provide robust empirical support for the pedagogical effectiveness of AI-enhanced instruction in improving Business English outcomes and learner engagement. This study explores innovative methods in language assessment and technology-enhanced language teaching by providing empirical validation of an AI-driven learning and assessment platform for Business English instruction.