A Corpus-Based Analysis of Verb Collocations in Human and AI-Generated IELTS Writing

In this study, it aims at examining the differences between human-generated and AI-generated texts in IELTS Writing Task 2. It especially focuses on lexical resourcefulness, grammatical accuracy, and contextual appropriateness. We analyzed 20 essays, including 10 human written ones by Chinese university students who have achieved an IELTS writing score ranging from 5.5 to 6.0, and 10 ChatGPT-4 Turbo-generated ones, using a mixed-methods approach, through corpus-based tools (NLTK, SpaCy, AntConc) and qualitative content analysis. Results showed that AI texts exhibited superior grammatical accuracy (0.4%–3% error rates for AI vs. 20%–26% for university students) but higher lexical repetition (17.2% to 23.25% for AI vs. 17.68% for university students) and weaker contextual adaptability (3.33/10- 3.69/10 for AI vs. 3.23/10 to 4.14/10 for university students). While AI’s grammatical precision supports its utility as a corrective tool, human writers outperformed AI in lexical diversity and task-specific nuance. The findings advocate for a hybrid pedagogical model that leverages AI’s strengths in error detection while retaining human instruction for advanced lexical and contextual skills. Limitations include the small corpus and single-AI-model focus, suggesting future research with diverse datasets and longitudinal designs.