The Impact of Adaptive Learning Based on Artificial Intelligence on Elementary School Students' Mathematics Learning Motivation

Arwasih (Universitas Pendidikan Indonesia)
Andika Arisetyawan (Universitas Pendidikan Indonesia)
Andhin Dyas Fitriani (Universitas Pendidikan Indonesia)

ETNOPEDAGOGI: Jurnal Pendidikan dan Kebudayaan
Vol. 3 No. 3 (2026): JULY 2026

This research aims to examine the effect of artificial intelligence-based adaptive learning on elementary school students' mathematics learning motivation. This research is quantitative research with the type of experimental research. The experimental design used was a quasi experiment involving class 4a students at SD Negeri Cileungsir as the experimental class (30 students) and class 4b students at SD Negeri Cileungsir as the control class (30 students). This research data is quantitative data collected using questionnaire techniques. The collected data was then analyzed using descriptive statistical test techniques by testing individual acquisition values ​​and classical averages. Individual and classical scores are then interpreted based on the categorization table. Next, the data were analyzed using inferential statistical test techniques using independent t-tests and paired t-tests. The research results show that adaptive learning based on artificial intelligence has a positive and significant effect on elementary school students' mathematics learning motivation. This is evident from research data which shows that there was a significant increase in the average score of students' mathematics learning motivation in the experimental class between before and after being given treatment, namely from 62.34 (low category) to 90.78 (very high category). Furthermore, the average posttest score for students' mathematics learning motivation in the experimental class showed 90.78 (very high category), while in the control class it was 67.12 (low category). This means that the mathematics learning motivation of students who use artificial intelligence-based adaptive learning is better. Inferential statistical tests were carried out in this study to test the research hypothesis. The results of the prerequisite tests are used as a basis for continuing hypothesis testing using parametric statistical techniques using the t-test type. The results of the independent t-test and paired t-test show that the significance value is smaller than the alpha value, namely 0.000 (<0.005). Based on these results, adaptive learning based on artificial intelligence can be used as an alternative to overcome the low motivation to learn mathematics in elementary school students.