Exploring Master’s Students' Paraphrasing and Synthesis Techniques: A Comparative Analysis with Artificial Intelligence-Based Text Generation
Aicila Lei L Forlales | Emelyn R. Villanueva
Discipline: Education
Abstract:
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 humancentric 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.
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