SECONDARY SCHOOL STUDENTS’ ACCEPTANCE OF ARTIFICIAL INTELLIGENCE IN SCIENCE LEARNING

Artificial Intelligence (AI) is increasingly transforming educational practices and offers promising opportunities to enhance science learning through personalized support, adaptive feedback, and access to diverse learning resources. Although Artificial Intelligence is increasingly being integrated into educational settings, empirical evidence regarding the factors influencing its acceptance among secondary school students, particularly in science learning, remains limited. This study investigates the predictive effects of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs on secondary school students' behavioral intention to use AI in science learning. A cross-sectional quantitative survey was conducted among 207 secondary school students from public and private schools in Casablanca, Morocco. Data were collected using a structured questionnaire measuring the core UTAUT constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention. The data were analyzed using descriptive statistics, Spearman correlation analysis, and multiple linear regression. The results indicate that students generally hold positive perceptions of AI in science learning. Regression analysis revealed that performance expectancy was the only significant predictor of behavioral intention (β = 0.366, p < 0.001), whereas effort expectancy, social influence, and facilitating conditions showed no statistically significant effects. The proposed model explained 20.4% of the variance in students' behavioral intention (R² = 0.204). These findings suggest that students' acceptance of AI depends primarily on their perception of its educational usefulness rather than on ease of use, social pressure, or facilitating conditions. The study extends current knowledge on AI acceptance in secondary science education by providing empirical evidence from the Moroccan context and offers practical implications for designing pedagogically meaningful AI-supported science learning environments.