This presentation examines the potential of the free version of GPT-4o as an AI-assisted tool for cross-textual error analysis in EFL academic writing. The dataset consisted of ten academic essays of approximately 600 words each written by second-year undergraduate students enrolled in an elective English course at a public university in Japan. The essays, focusing on disease-related risk factors, were analyzed collectively using structured prompts designed to elicit systematic error categorization, frequency reporting, and simple feedback. The analysis identified error types widely documented in SLA research on Japanese learners; however, article misuse accounted for approximately 33% of all identified errors, followed by preposition errors (18%) and subject–verb agreement errors (15%). Rather than discovering new categories, the study evaluates whether ChatGPT can rapidly aggregate patterns across multiple texts, quantify their relative distribution, and prioritize high-impact errors for instruction. From a CALL perspective, the primary contribution of this study lies in instructional mediation. Through the use of AI for common writing error detection and correction, teachers can spend more time focusing on deeper dimensions of writing, such as the clarity of arguments, coherence, and logical structure.