The Effect of Artificial Intelligence-Assisted Science Teaching Modules Based on the SSCS Model on Increasing Student Learning Motivation

Mohamad Rivaldi  Bulilingo; Ritin Uloli; Muhammad  Yusuf

doi:10.63922/ijeter.v3i04.2639

Mohamad Rivaldi Bulilingo Postgraduate Program in Science Education, Gorontalo State University, Gorontalo, Indonesia
Ritin Uloli Postgraduate Program in Science Education, Gorontalo State University, Gorontalo, Indonesia
Muhammad Yusuf Postgraduate Program in Science Education, Gorontalo State University, Gorontalo, Indonesia

DOI: https://doi.org/10.63922/ijeter.v3i04.2639

Keywords: AI-assisted learning, SSCS model, science education, learning motivation, student engagement

Abstract

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.

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