Exploring Master’s Students' Paraphrasing and Synthesis Techniques: A Comparative Analysis with AI-Based Text Generation

This study examines the synthesis and paraphrasing strategies employed by Master’s students compared to AI-based text generation tools like Chat GPT. A qualitative methodology was employed, incorporating document analysis and thematic analysis of responses from Master’s students and AI-generated outputs. Findings revealed that Master’s students adopt a human-centric approach, characterized by critical evaluation, contextual understanding, and cohesive narrative construction, integrating personal insights into their synthesis. In contrast, AI tools prioritize efficiency and scalability but lack critical analysis and depth, often producing generic outputs. Quantitatively, 83% of students demonstrated reliance on personalized paraphrasing methods, blending diverse sources into coherent arguments, while AI-generated texts showcased rapid processing but limited capacity for nuanced interpretation. Notably, Master’s students outperformed AI tools in critical evaluation and integration of multiple perspectives, while AI tools excelled in speed and scalability. The study highlights the complementary nature of human and AI-driven synthesis approaches. Recommendations include integrating human judgment with AI capabilities, ethical considerations for AI use, and fostering digital literacy among educators and learners. By leveraging the strengths of both methods, researchers can achieve deeper insights and promote innovative practices in academic writing. This study provides valuable implications for enhancing academic writing pedagogy, advancing AI tools, and fostering interdisciplinary collaboration in educational contexts.