Implementation of Innovative Artificial Intelligence Cognitions With Problem-Based Learning Guided Tasks to Enhance Students’ Performance in Science

This research conceived multivariate learning concepts and aimed at the implementation of integrating artificial intelligence (AI) life learning of face recognition, self-driving, and robots’ situational issues into problem-based learning (PBL) in natural science. This research put forward problem-solving and group cooperation in which students worked together to complete learning tasks from encountering problems to planning problem-solving. All 127 participants engaged in this research from the Hungkuo Delin University of Technology in Taiwan. 7 experts helped construct the content validity, construct validity, and reliability through instrument developments of 5 open-ended test questions and 31 learning attitude items. The statistical analyses revealed that those students improved their scientific cognitive skills of problem-solving by quantitative analyzes and presented their logical reasoning and activation ability by qualitative ones. In addition, the descriptive statistical analysis of learning attitude revealed that students had positive thinking attributes for the application of cross-disciplinary PBL and AI new knowledge. These findings had implications for the AI cognitions with PBL educational environment to increase innovation in the classroom. Pedagogical suggestions presented that students’ learning growth in the environment requires long-term cultivation to exhibit significant learning outcomes in the future. Keywords: artificial intelligence, cross-disciplinary, problem-based learning, situational issues