The Relationship Between Artificial Intelligence Use and Learning Motivation Among Middle and High School Students in Israel

While artificial intelligence (AI) integration in education has attracted considerable attention, research examining its impact on student motivation remains limited. This study investigates the relationship between AI use for learning purposes and learning motivation among 207 Israeli middle and high school students. Data were collected using two self-report instruments: a questionnaire measuring AI use frequency and perceived educational value, and a learning motivation questionnaire based on self-determination theory. Pearson correlations, descriptive statistics, and group comparisons examined associations between variables and differences across school levels and gender. Findings revealed a statistically significant positive correlation between AI use for learning and students’ learning motivation, with students reporting higher AI use demonstrating greater learning motivation. This association was significantly stronger among middle school students compared to high school students, while no gender differences emerged. Despite limitations including moderate internal reliability of the motivation scale, convenience sampling, and the correlational design preventing causal inferences, results suggest AI-supported learning environments may be particularly effective for fostering motivation during early adolescence. The directionality of the relationship remains unclear—whether AI use enhances motivation or motivated students are more inclined to adopt AI tools. These findings contribute to emerging literature on AI in education by highlighting motivational implications and emphasizing the need for developmentally sensitive AI integration. Future research should employ longitudinal and experimental designs to clarify causal mechanisms.

Authors

Institutions

Publication Details

Journal
Education Sciences
Published
2026-04-13
DOI
https://doi.org/10.3390/educsci16040617
Primary Topic
Motivation and Self-Concept in Sports
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Relationship Between Artificial Intelligence Use and Learning Motivation Among Middle and High School Students in Israel

N. Davidovitch, Roman Yavich
Education Sciences
Motivation and Self-Concept in Sports
article

The Relationship Between Artificial Intelligence Use and Learning Motivation Among Middle and High School Students in Israel

N. Davidovitch, Roman Yavich
article en

Abstract

While artificial intelligence (AI) integration in education has attracted considerable attention, research examining its impact on student motivation remains limited. This study investigates the relationship between AI use for learning purposes and learning motivation among 207 Israeli middle and high school students. Data were collected using two self-report instruments: a questionnaire measuring AI use frequency and perceived educational value, and a learning motivation questionnaire based on self-determination theory. Pearson correlations, descriptive statistics, and group comparisons examined associations between variables and differences across school levels and gender. Findings revealed a statistically significant positive correlation between AI use for learning and students’ learning motivation, with students reporting higher AI use demonstrating greater learning motivation. This association was significantly stronger among middle school students compared to high school students, while no gender differences emerged. Despite limitations including moderate internal reliability of the motivation scale, convenience sampling, and the correlational design preventing causal inferences, results suggest AI-supported learning environments may be particularly effective for fostering motivation during early adolescence. The directionality of the relationship remains unclear—whether AI use enhances motivation or motivated students are more inclined to adopt AI tools. These findings contribute to emerging literature on AI in education by highlighting motivational implications and emphasizing the need for developmentally sensitive AI integration. Future research should employ longitudinal and experimental designs to clarify causal mechanisms.

Education SciencesVol. 16(4)
Ariel University (IL)
Ariel University
Quality Education
Openalex Percentile: Top 21%
Motivation and Self-Concept in Sports
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.