Vol-11,Issue-4,July - August 2026
Author: Gemma Genabe Sordilla Calo-oy
Abstract: This study assessed the effectiveness of ChatGPT AI-driven scaffolding in enhancing the lesson plan writing performance of English pre-service teachers at the University of Cebu-Main Campus, school year 2025–2026 as basis of an enhanced learning material. The study employed a quasi-experimental design through random sampling in selecting fifty (50) participants, equally divided into twenty-five (25) participants for the control group and another twenty-five (25) for the experimental group. A lesson plan template and a standardized rubric evaluating organization, content, grammar, and mechanics served as the primary research instruments. Data were analyzed using descriptive and inferential statistical tools, including the t-test for dependent and independent samples. Findings revealed that the pre-test data indicated both groups initially struggled with structural organization and failed to reach high proficiency, while post-test results showed that the control group exhibited no statistically significant change and the experimental group demonstrated a significant improvement toward proficiency across all categories. Hypotheses testing confirmed a significant difference in the post-test performance of the experimental group, with p-values ranging from 0.000 to 0.017, well below the alpha level of 0.05, leading to the rejection of the null hypothesis (Ho), which means that the intervention provided to the experimental group was highly effective in enhancing their ability to structure, articulate, and refine lesson plans. In conclusion, ChatGPT AI-driven scaffolds are highly effective in refining lesson plan writing performance compared to traditional methods.
Keywords: English Teaching, ChatGPT AI-driven scaffolding, writing performance, quasi-experimental quantitative design, University of Cebu-Main Campus, Cebu City, Philippines
Article Info: Received: 05 Jul 2026; Received in revised form: 01 Aug 2026; Accepted: 06 Aug 2026; Available online: 10 Aug 2026
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