A Comparative Genre Analysis of Human-Written and Ai-Generated Research Abstracts

Abstract

This comparative study explores the distinctive generic features between abstracts written by human authors and those generated by artificial intelligence tools through the genre analysis methods. Over-reliance on such external mechanisms and plagiarism are perennial issues that affect the global academia particularly in regards to the widespread dependence on artificial intelligence. The study compares ten research abstracts written by postgraduate master students specialising in English Language and Literature from a Malaysian public university to AI-generated abstracts produced using Chat Generative Pre-Trained Transformer 3, also known as ChatGPT. The study looks into the frequency and quality of key elements or moves such as statements of objectives, methods, results, and contextualisation to determine their recurrence patterns. Findings indicate that human-inscribed abstracts reveal a more stable and thorough presentation, highlighting contextualisation and inclusive results, while AI-generated abstracts possess clarity in statements of objectives with minimal coverage on results and contextual details. The findings in this research thus recommend for the development of an innovative method of detecting AI-generated content written by students using the genre analysis approach. It also emphasises the necessity for specialised teacher training and rigorous evaluation criteria to preserve academic integrity and overcome the limitations of using AI in academic writing.