<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pei-yu Chen</style></author><author><style face="normal" font="default" size="100%">Yuan-Chen Liu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">IMPACT OF AI ROBOT IMAGE RECOGNITION TECHNOLOGY ON IMPROVING STUDENTS’ CONCEPTUAL UNDERSTANDING OF CELL DIVISION AND SCIENCE LEARNING MOTIVATION</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Baltic Science Education</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">cell division</style></keyword><keyword><style  face="normal" font="default" size="100%">image recognition technology</style></keyword><keyword><style  face="normal" font="default" size="100%">learning by teaching</style></keyword><keyword><style  face="normal" font="default" size="100%">science learning motivation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April/2024</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://oaji.net/articles/2023/987-1713272661.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">Continuous</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 &quot;Learning by Teaching&quot; 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 &quot;Cell Division Two Stage Diagnostic Test&quot; and the &quot;Science Learning Motivation Scale.&quot; The results indicate that learning through teaching AI robot image recognition technology is more effective than textbook learning in enhancing students’ comprehension of the &quot;cell division&quot; concept and boosting motivation to learn science.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">208-220</style></section></record></records></xml>