AI-SCIENCE LEARNING WITH A DEEP LEARNING APPROACH TO IMPROVE INCLUSIVE STUDENT LEARNING OUTCOMES

Abstract

Purpose - This study aimed to investigate the effectiveness of implementing AI-based science learning integrated with a deep learning approach in improving the academic achievement of inclusive students. This study also evaluated the impact of deep learning strategies on inclusive students' conceptual understanding, engagement, and critical thinking skills in science learning.

Methodology – This study uses a quantitative approach with 25 elementary school students, including inclusive students who have adopted or tried AI-based or deep learning-based educational tools in science lessons.

Findings – The results of this study indicate that the implementation of AI-based science learning and deep learning positively impacted the academic achievement of elementary school students, including those with special needs. The average post-test score for students increased by 21%, comparable to their peers. Observational data show that inclusive students are more actively engaged during learning. AI features such as voice support, visual simulations, and interactive tasks helped reduce learning barriers, boost their confidence, and increase their participation in science learning activities. Student feedback collected during the learning sessions indicated that most students found the AI-based learning tools enjoyable and easy to use. Paired-sample t-test results showed a statistically significant difference between pre- and post-test scores (p < 0.05), confirming the effectiveness of AI-based learning and deep learning in improving students' science learning outcomes.

Contribution – The results of this study demonstrate that AI-based learning tools, integrated with deep learning strategies, are highly effective in supporting inclusive education. AI adapts to diverse learning needs and can reduce barriers for students with special needs in the classroom, benefiting learning for diverse students.