AI Utilization and User Satisfaction among Science Teachers and Students: A Structural Equation Modeling Analysis

Abstract

The rapid advancement of artificial intelligence has significantly influenced contemporary educational practices, offering opportunities to enhance teaching and learning while raising concerns about effective and ethical integration. This study examines AI utilization and user satisfaction among science teachers and students, and analyzes its effects on satisfaction, usefulness, ease of use, and reliability. A quantitative research design involved 242 STEM students and 22 science teachers in the Division of Tacurong City. Data were collected through structured questionnaires and analyzed using descriptive statistics and Structural Equation Modeling. Results showed high AI utilization among students (x̄ = 3.79) and very high utilization among teachers (x̄ = 4.21). Satisfaction was high among students (x̄ = 3.87) and very high among teachers (x̄ = 4.38). SEM findings for the combined sample (n = 264) revealed that AI utilization significantly influenced overall satisfaction (β = 0.465, R² = 0.217, p < .001), usefulness (β = 0.457, R² = 0.208, p < .001), ease of use (β = 0.336, R² = 0.113, p < .001), and reliability (β = 0.257, R² = 0.066, p < .001). Among students (n = 242), AI utilization also showed significant positive effects on satisfaction and all dimensions. Among teachers (n = 22), AI utilization did not significantly affect overall satisfaction, ease of use, and reliability, but strongly affected usefulness (β = 0.576, R² = 0.332, p = .005). The findings indicate that AI is widely utilized and positively perceived among science teachers and students.