Integrating Artificial Intelligence in Differentiated Learning: Implications of Teacher Competence for Student Achievement and Motivation

Nurdin; Saripuddin; Alim Bahri; Andi Aris Mattunruang; Muhlis

doi:10.63922/ijeter.v4i02.4067

Nurdin Universitas Patompo, Makassar, Indonesia
Saripuddin Universitas Patompo, Makassar, Indonesia
Alim Bahri Universitas Patompo, Makassar, Indonesia
Andi Aris Mattunruang Universitas Patompo, Makassar, Indonesia
Muhlis Universitas Patompo, Makassar, Indonesia

DOI: https://doi.org/10.63922/ijeter.v4i02.4067

Keywords: Differentiated Learning, Teacher Competence, Artificial Intelligence (AI), Student Learning Achievement, Academic Motivation

Abstract

This study aims to examine the effectiveness of an Artificial Intelligence (AI)-based Differentiated Learning model in improving student achievement and academic motivation in senior high schools in Makassar. Employing a quantitative approach with a quasi-experimental design, the research involved students from several leading schools who were divided into experimental and control groups. The instruments used included achievement tests, academic motivation questions , and teacher competence observation sheets related to AI integration. The findings reveal that students in the experimental group who experienced AI-based differentiated learning achieved better posttest scores and higher academic motivation compared to those in the control group. Statistical analyzes confirmed significant differences between the two groups, while further examination indicated that teacher competence and student motivation play an essential role in shaping learning outcomes. These results highlight that the success of AI-based learning is not solely dependent on technology but is strongly influenced by teachers' ability to adapt and effectively utilize AI in differentiated learning contexts. The study underscores the importance of continuous professional development for teachers, the development of contextually relevant teaching modules, and adequate digital infrastructure support. Furthermore, this research provides empirical contributions to the advancement of adaptive and technology-based learning in Indonesia, with implications for both policy and practice.

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