A Comprehensive Analysis of Generative AI Integration in Enhancing Syntactic Complexity for Undergraduate Academic Writing

Abstract

This study explores how Generative Artificial Intelligence (GAI) can be integrated to improve the syntactic complexity of academic writing of undergraduate students in the English as a Second Language (ESL) setting. Syntactic complexity is one of the primary indicators of writing proficiency, yet an area of persistent difficulty for undergraduate students. This is typically characterized by the employment of a narrow range of sentence structures and an overuse of simple sentence structures. This study used a quasi-experimental design and involved undergraduate students in writing tasks with and without AI support. Data was collected in the form of writing samples before and after the intervention and analyzed using a set of predetermined linguistic measures, including the mean length of T-unit, clauses per sentence, and subordination index. Using AI writing tools showed the greatest increase in the students' writing syntactic sophistication. There was considerable increase in the number of students using writing tools to produce complex and compound sentences and there were more integrated clauses. Improvements in coherence and syntactic variety and in the overall structure and organization of the students' writing were noted in the qualitative data. The study also showed the potential Generative AI has as a scaffolding tool in the advancement of writing proficiency and a tool for helping students to develop a more sophisticated control of the language. Guided use of AI writing tools in the classroom setting, the study argues, helps to minimize the risk of students becoming overly reliant on the tools and of the tools discouraging engagement with the material. The study also adds to the expansive body of research on technology and language learning and offers recommendations for using AI technology in the writing instruction of undergraduate students.