A Taxonomy of Errors in English as she is spoke: Toward an AI-Based Method of Error Analysis for EFL Writing Instruction

Background Recent developments in artificial intelligence (AI), particularly Large Language Models (LLMs), have shown promise in automating previously unavailable aspects of student writing assessment and providing detailed, individuated feedback. Our previous research demonstrated that AI systems can reliably assess student writing using standardized rubrics, achieving consistency 2 rates of over 99% over five iterations (Heywood & Carrier, 2024). However, while these systems excel at providing holistic assessment using broad categories, their potential to provide detailed, granular feedback about specific writing errors has not yet been fully explored . This study builds upon our earlier work by developing and testing a sophisticated error classification system that can identify, categorize, and describe writing errors at both the word and sentence levels. The system employs a detailed taxonomy of errors based on established linguistic theory in the area of error classification (Corder, 1967, 1975, 1981; Richards, 1971, 1974; James, 1998). The AI analysis is implemented through carefully designed API calls to Claude 3.5 Sonnet in Python. With this enhanced error classification system, the present study analyzes an error ridden dialogue from an infamous text, English as she is spoke (Fonseca et al., 2004). We also provide the results of a review of the AI analysis by a human panel of experts.