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<p>This study explores the effectiveness of Generative AI (GenAI) versus teacher feedback in senior high school English reading-based continuation writing instruction. A 6-week quasi-experiment was conducted with 260 11th-grade Chinese students, who were divided into low-proficiency and high–proficiency groups. Each group was further split into AI feedback and teacher feedback subgroups. Results show GenAI significantly boosted low-proficiency students’ overall writing performance (ΔM = 6.1, δ = 0.73) via timely, detailed grammar and vocabulary feedback. For high-proficiency students, GenAI improved basic skills, but teachers outperformed GenAI in improving students’ higher-order competencies like content relevance and logical coherence. Both feedback forms reached high scoring consistency (ICC = 0.84). Finding suggest that GenAI and teacher feedback are complementary, and stratified integration (GenAI for basic skills, teachers for higher-order guidance) optimizes outcomes, informing better teaching practices in reading-based continuation writing classes for senior high school in China.</p>

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Does generative AI narrow or widen performance gaps? a stratified quasi-experiment on reading-based continuation writing in senior high school english

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Li Jiang,
  • Lixin Huang,
  • Meng Wei,
  • Ruoyu Wang

摘要

This study explores the effectiveness of Generative AI (GenAI) versus teacher feedback in senior high school English reading-based continuation writing instruction. A 6-week quasi-experiment was conducted with 260 11th-grade Chinese students, who were divided into low-proficiency and high–proficiency groups. Each group was further split into AI feedback and teacher feedback subgroups. Results show GenAI significantly boosted low-proficiency students’ overall writing performance (ΔM = 6.1, δ = 0.73) via timely, detailed grammar and vocabulary feedback. For high-proficiency students, GenAI improved basic skills, but teachers outperformed GenAI in improving students’ higher-order competencies like content relevance and logical coherence. Both feedback forms reached high scoring consistency (ICC = 0.84). Finding suggest that GenAI and teacher feedback are complementary, and stratified integration (GenAI for basic skills, teachers for higher-order guidance) optimizes outcomes, informing better teaching practices in reading-based continuation writing classes for senior high school in China.