Generative AI Empowering College English Writing: An Empirical Study on Syntactic Complexity and Lexical Richness

Junyi Wang

doi:10.47297/taposatWSP2633-456902.20260708

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Generative AI Empowering College English Writing: An Empirical Study on Syntactic Complexity and Lexical Richness

7卷第8期

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Generative AI Empowering College English Writing: An Empirical Study on Syntactic Complexity and Lexical Richness
Generative AI Empowering College English Writing: An Empirical Study on Syntactic Complexity and Lexical Richness
School of Foreign Languages, Guangzhou College of Commerce, Guangzhou, Guangdong, 511363, China
2026年7卷第8期页码：11-15
纸质出版：2026-08-30
DOI：10.47297/taposatWSP2633-456902.20260708
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Junyi Wang. (2026). Generative AI Empowering College English Writing: An Empirical Study on Syntactic Complexity and Lexical Richness. 科技理论与实践（英文版）, 7(8), 11-15.

Wang Junyi.Generative AI Empowering College English Writing: An Empirical Study on Syntactic Complexity and Lexical Richness[J].Theory and Practice of Science and Technology,2026,07(08):11-15.
Junyi Wang. (2026). Generative AI Empowering College English Writing: An Empirical Study on Syntactic Complexity and Lexical Richness. 科技理论与实践（英文版）, 7(8), 11-15. DOI： 10.47297/taposatWSP2633-456902.20260708.

Wang Junyi.Generative AI Empowering College English Writing: An Empirical Study on Syntactic Complexity and Lexical Richness[J].Theory and Practice of Science and Technology,2026,07(08):11-15. DOI： 10.47297/taposatWSP2633-456902.20260708.

摘要

Abstract

Drawing on the Output Hypothesis and the Noticing Hypothesis

this study employs a single-group pretest-posttest quasi-experimental design with 108 first-year non-English major students at a college to quantitatively analyze the effects of "AI + teacher" dual-line feedback on syntactic complexity and lexical richness in English writing

using the NeoSCA tool. Six indicators—MLS

MLC

C/S

LS1

and MSTTR—were selected. Results showed that MLS (Z = 6.415

p < 0.001)

MLC (t = -11.726

p < 0.001)

and LD (t = -3.827

p < 0.001) improved significantly; C/S (t = 6.247

p < 0.001) decreased significantly; while MSTTR (p = 0.102) and LS1 (p = 0.445) showed no significant differences. These findings indicate that dual-line feedback is effective in promoting phrase expansion and content-word density enhancement

but does not facilitate clause embedding or advanced lexical expansion

exhibiting a selective improvement pattern characterized by "increased length without structural complexification." This study provides purely quantitative empirical evidence for AI-assisted English writing instruction.

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references

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