Teacher-Guided AI-Supported Digital Ecosystem Learning in Primary Science: A Quasi-Experimental Study

Despite the growing use of artificial intelligence (AI) in science education, little is known about the motivational value of AI-supported digital tools in upper-primary ecosystem science learning. This quasi-experimental study examined whether participation in a teacher-guided digital ecosystems unit integrating AI-supported elements and interactive non-AI tools was associated with sixth-grade students’ ecosystem achievement, interest in science, attitudes toward science, and science self-efficacy. Four sixth-grade classes in an Israeli elementary school (123 students) participated. The experimental group completed six 45-min lessons; the control group studied the same content without the AI-supported components and integrated digital sequence. Students completed pretest and posttest questionnaires and an ecosystem achievement test; the experimental group also completed a satisfaction questionnaire. Semi-structured interviews were conducted with 10 students from the experimental group. Baseline-adjusted analyses indicated higher post-intervention achievement and motivational outcomes in the experimental group. Boys reported higher interest and self-efficacy than girls, and mothers’ education was positively associated with interest and attitudes. Within the experimental group, satisfaction was positively related to all motivational outcomes and significantly predicted self-efficacy. Interview themes highlighted visualization, feedback, collaboration, and occasional cognitive and technical challenges. Overall, this teacher-guided instructional package was associated with more favorable outcomes under classroom conditions in schools.