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

Bibliographic Data

ID22049359
AuthorsRoman Yavich (0000-0002-3864-0068, Ariel University, corresponding author), Nitza Davidovitch (0000-0001-7273-903X, Ariel University)
Year2026
Volume16
Issue4
Pages617
Publication date2026-04-13
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci16040617
OpenAlexW7154004117
LanguageEN
References cited14

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

Descriptive statistics · Intrinsic motivation · Longitudinal study · Validity · Educational Games and Gamification · Ethics and Social Impacts of AI · Motivation and Self-Concept in Sports

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