A Comparative Analysis of Coding and Artificial Intelligence Learning Implementation on Teacher Competence, Student Learning Interest, and Motivation in Elementary Schools

Abstract

This study aims to analyze the comparative differences and effects of coding and artificial intelligence (AI) learning implementation on teacher competence, student learning interest, and student learning motivation at elementary schools in Kayu Aro Barat District, Kerinci Regency. A quantitative comparative explanatory (ex post facto) design was employed. The population consisted of all Grade V teachers and students across 17 elementary schools based on data from the Kerinci Regency Education Office (2024). Using proportionate stratified random sampling, eight schools were selected—four implementing coding and AI (experimental group) and four not implementing it (control group)—involving 8 teachers and 120 fifth-grade students. Data were collected through a structured Likert-scale questionnaire (1–5), observation, documentation, and semi-structured interviews. Analysis employed MANOVA (Y2 and Y3), independent sample t-test, and multiple linear regression. Results showed: (1) significant differences in teacher competence (t = 7.215; p = .000; mean diff. = 13.75), learning interest (t = 9.123; p = .000; mean diff. = 12.20), and learning motivation (t = 8.764; p = .000; mean diff. = 12.10); (2) significant positive effects on learning interest (β = 0.748; R² = 0.560) and learning motivation (β = 0.751; R² = 0.564); and (3) simultaneous multivariate effects on student learning interest and motivation (Wilks' Λ = 0.538; F = 50.20; df = 2, 117; p = .000). These findings confirm that structured coding and AI implementation substantially enhances elementary school learning quality in Kayu Aro Barat District.