The Effect of Artificial Intelligence-Assisted Science Teaching Modules Based on the SSCS Model on Increasing Student Learning Motivation - Bulilingo, Mohamad Rivaldi; Uloli, Ritin; Yusuf, Muhammad (International Journal of Technology and Education Research, 2025)
This study aims to examine the effect of an AI-assisted Science module based on the SSCS (Search, Solve, Create, Share) model on students’ learning motivation. A one-group pretest–posttest design was applied to a class of 30 seventh-grade students. Data were collected using a validated Likert-scale questionnaire on learning motivation. Normality tests (Shapiro–Wilk) confirmed that the pretest and posttest scores were normally distributed, allowing the use of a paired sample t-test. The pretest results showed an average learning motivation score of 64.17% (Fair), indicating moderate engagement. Indicators such as willingness and desire to succeed (55%), drive and need to learn (57%), and appreciation in learning (65%) were relatively low, suggesting that students were passive and lacked intrinsic motivation. After implementing the AI-assisted SSCS module, the posttest results demonstrated a significant improvement, with an average score of 79.5% (Good). All indicators increased, including willingness to succeed (80%), drive to learn (77%), and future aspirations (81%, Excellent). The paired t-test confirmed a statistically significant increase in learning motivation (t = 16.23, p < 0.05). The findings indicate that integrating AI with the SSCS learning model enhances students’ engagement, intrinsic motivation, and participation in learning activities. This study suggests that AI-assisted, structured, and interactive learning modules can be an effective strategy to improve both cognitive and affective outcomes in science education.