Impact of an AI-driven teacher dashboard on student performance in inquiry-based science learning

ERNO II, PERFECTO L. and BERRY, ERWIN B. (2025) Impact of an AI-driven teacher dashboard on student performance in inquiry-based science learning. International Journal of Science and Research Archive, 15 (2). pp. 135-138. ISSN 2582-8185

This research examined the use of Poll Everywhere, an AI-based teacher dashboard, as a part of inquiry-based learning (IBL) for the Grade 7 students at Carrascal National High School during the 2024–2025 school year. Using a quasi-experimental research design with two groups of students; an experimental group of 46 students using AI-driven instruction and a control group of 43 students using direct instruction without AI support, results showed that both groups of students had developed in their student learning outcomes for academic performance after pre-test to post-questionnaire analysis; however, the experimental group developed significantly larger than the control group, indicating a stronger impact of the teacher's use of the AI-based dashboard on student learning outcomes compared to their use of a more traditional instructional approach with students. The research also identified some of the benefits of using AI-driven instruction and learning with inquiry-based science learning. Specifically, student engagement with their learning was deeper due to the immediate and interactive delivery of feedback from Poll Everywhere's dashboard, as students had an opportunity to concentrate on better academic performance and learning. Overall, the study indicates that learning and development in science can be significantly accelerated by AI-driven tools, including Poll Everywhere, that will develop student knowledge compared with teaching using a more conventional instructional approach that promotes active engagement with learning and better retention of content.