Article: A generative adversarial error correction system for English writing Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.45 pp.42 - 61 Abstract: This paper addresses the prevalent issue of 'over-correction' in current AI English writing tools - where correct personalised expressions are often misidentified as errors - by proposing an innovative adversarial generative error correction system. This system mimics the 'teacher-student interaction' mechanism: one network attempts to modify sentences, while another network judges the necessity of such modifications, thereby achieving more precise error correction. For instance, a system might incorrectly 'correct' a stylistically chosen active-voice sentence (e.g., 'our team analysed the data') into a passive construction ('the data was analysed by our team'), thereby altering the author's intended emphasis. Another common over-correction involves replacing a correctly used but less frequent disciplinary term with a more common, yet less precise, synonym. In public dataset evaluations, the system achieves an 89.5% correction accuracy - a significant improvement over traditional rule-based methods (approximately 70.2%) - while maintaining an over-correction rate of only 12.1%, substantially lower than that of a general-purpose large model (approximately 35.7%). This demonstrates the advantages of adversarial generation methods in understanding writing intent and context, providing an effective pathway for developing smarter, more human-like writing assistance tools. Inderscience Publishers - linking academia, business and industry through research

Title: A generative adversarial error correction system for English writing

Authors: Wen Zhang

Addresses: Basic Courses Teaching Department, Xinxiang Vocational and Technical College, Xinxiang, 453000, China

Abstract: This paper addresses the prevalent issue of 'over-correction' in current AI English writing tools - where correct personalised expressions are often misidentified as errors - by proposing an innovative adversarial generative error correction system. This system mimics the 'teacher-student interaction' mechanism: one network attempts to modify sentences, while another network judges the necessity of such modifications, thereby achieving more precise error correction. For instance, a system might incorrectly 'correct' a stylistically chosen active-voice sentence (e.g., 'our team analysed the data') into a passive construction ('the data was analysed by our team'), thereby altering the author's intended emphasis. Another common over-correction involves replacing a correctly used but less frequent disciplinary term with a more common, yet less precise, synonym. In public dataset evaluations, the system achieves an 89.5% correction accuracy - a significant improvement over traditional rule-based methods (approximately 70.2%) - while maintaining an over-correction rate of only 12.1%, substantially lower than that of a general-purpose large model (approximately 35.7%). This demonstrates the advantages of adversarial generation methods in understanding writing intent and context, providing an effective pathway for developing smarter, more human-like writing assistance tools.

Keywords: English writing assistance; adversarial generative networks; grammar correction; overcorrection.

DOI: 10.1504/IJICT.2026.153515

International Journal of Information and Communication Technology, 2026 Vol.27 No.45, pp.42 - 61

Received: 15 Dec 2025
Accepted: 17 Jan 2026
Published online: 12 May 2026 *

Title: A generative adversarial error correction system for English writing

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