Enhancing Junior High School Teaching Performance Through AI-Supported Deep Learning Approaches - Budi Tri Cahyono; Fatma Sukmawati; Eka Budhi Santosa; Relly Prihatin; Suparmi; Taufiq Subhanul Qodr (JTP - Jurnal Teknologi Pendidikan, 2025)
supported deep learning approaches in improving the teaching performance of junior high school teachers. With the increasing demand for 21st-century teaching competencies, there is a need for innovative instructional strategies that enhance teacher effectiveness and engagement. A quantitative quasi-experimental method was employed, involving pre-tests and post-tests administered to both control and experimental groups. The study involved 64 junior high school teachers across science and social studies subjects. Teaching performance was assessed using structured observation and evaluation tools covering lesson planning, implementation, and assessment practices. Data analysis was conducted using t-tests and normalized gain (n-gain) scores. The findings demonstrated that the teachers in the experimental group—who utilized AI-supported deep learning approaches—showed significantly greater improvement in teaching performance compared to the control group. The intervention was classified as “effective” in fostering meaningful, interactive, and future-ready instructional practices. The study concludes that integrating AI-supported deep learning strategies can substantially enhance teaching quality. It is recommended that educational institutions incorporate these approaches into ongoing professional development initiatives to better prepare teachers for modern educational demands. The findings demonstrated that the teachers in the experimental group showed significantly greater improvement in teaching performance.