Effectiveness of Artificial Intelligence and Data Literacy in Case-Based Learning on the Ability to Write Toulmin-Model Argumentative Essays

Abstract

This study aims to examine the effectiveness of integrating Artificial Intelligence (AI) and data literacy in Case-Based Learning (CBL) on students’ ability to write argumentative essays using the Toulmin model. The research design used a pre-experimental One-Group Pretest-Posttest Design with 22 student participants. The data were collected through argumentative essay writing tests administered before and after the treatment, and analyzed using the Paired-Samples t-Test. The results of the study showed a significant increase between the pretest score (M=53.59) and the posttest score (M=71.27), with a Sig. (2-tailed) value of 0.000<0.05. All Toulmin indicators increased, especially backing (56.1%) and rebuttal (46.8%), indicating that students’ ability to construct arguments based on evidence and critical analysis became stronger. The study concludes that integrating CBL, AI (ChatGPT), and data literacy effectively strengthens students’ academic argumentative abilities while supporting learning outcomes aligned with Outcome-Based Education (OBE) and the demands of twenty-first-century skills.