IMPACT OF AI ROBOT IMAGE RECOGNITION TECHNOLOGY ON IMPROVING STUDENTS’ CONCEPTUAL UNDERSTANDING OF CELL DIVISION AND SCIENCE LEARNING MOTIVATION

This study explored the integration of neural networks and artificial intelligence in image recognition for object identification. The aim was to enhance students' learning experiences through a "Learning by Teaching" approach, in which students act as instructors to train AI robots in recognizing objects. This research specifically focused on the cell division unit in the first grade of lower-secondary school. This study employed a quasi-experimental research design involving four seventh-grade classes in a rural lower-secondary school. The experimental group (41 students) were taught via an AI robot image recognition technology, whereas the control group (40 students) were taught via a more conventional textbook-centered approach. The research followed a pre-test design, with three classes lasting 45 min each, totaling 135 min of teaching time over two weeks. Evaluation tools include the "Cell Division Two Stage Diagnostic Test" and the "Science Learning Motivation Scale." The results indicate that learning through teaching AI robot image recognition technology is more effective than textbook learning in enhancing students' comprehension of the "cell division" concept and boosting motivation to learn science.