Analisis Proses Berpikir Tingkat Tinggi Berbasis STEM Terintegrasi Artificial Intelligence pada Mata Kuliah Statistik Dasar

Abstract

This study aims to analyze students' Higher-Order Thinking Skills (HOTS) within the context of an AI-integrated, STEM-based Basic Statistics course and to describe the role of AI in supporting the development of these skills. A qualitative, descriptive research approach was employed. The subjects consisted of three students from the Mathematics Education program enrolled in the Basic Statistics course, selected purposively based on high, medium, and low ability levels. Data were collected through observation, in-depth interviews, documentation, and HOTS-based assignment sheets. Data analysis followed the Miles and Huberman model—comprising data reduction, data display, and conclusion drawing—while data validity was verified through source and method triangulation. The results indicate that high-ability students were capable of critically analyzing, evaluating, and reflecting on statistical problem-solving processes and effectively utilizing AI as a learning aid. Medium-ability students demonstrated an understanding of concepts and the ability to use technology, though their evaluative and creative skills remained limited. Meanwhile, low-ability students tended to rely on procedural approaches, example solutions, and instant answers, showing suboptimal performance in contextual analysis and reflection. AI played a role in facilitating conceptual understanding, data analysis, answer evaluation, and the development of critical thinking and problem-solving skills; however, its use required a critical mindset to avoid dependency. This study provides empirical insights into the characteristics of students' HOTS in an AI-integrated, STEM-based Basic Statistics course, offering a reference for developing instructional strategies aimed at enhancing higher-order thinking skills in higher education.