The Effect of Artificial Intelligence in Adaptive Learning on Improving Student Understanding in Elementary School
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Background. Advances in artificial intelligence (AI) technology have presented various innovative opportunities in the world of education, especially in the development of adaptive learning systems. The diverse understanding of elementary school students and the need for appropriate learning approaches make AI-based learning a promising alternative to improving learning effectiveness.
Purpose. This study aims to determine the effect of the application of artificial intelligence in adaptive learning systems on improving student understanding at the elementary school level. The main focus is to see how much this system contributes in accommodating differences in learning styles and students' ability to understand the subject matter.
Method. The research method used was a pseudo-experiment with a pretest-posttest control group design. The study population consisted of grade V students at an elementary school in Indonesia, with purposive sampling techniques to determine the experimental and control groups. The instrument is in the form of a concept understanding test and observation of the learning process.
Results. The results showed that students who learned with AI-based adaptive systems experienced a significant increase in understanding compared to the control group. The average posttest score of the experimental group was higher with a more even increase. Case studies also show higher learning engagement and increased student motivation.
Conclusion. The conclusion of this study states that the application of AI in adaptive learning has great potential in improving student understanding, especially with a personalized approach to material and adjusted learning speed. This technology is able to effectively answer the challenge of differentiating learning at the elementary level.
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