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

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.

Authors

Publication Details

Journal
International Journal of Science and Research Archive
Published
2025-05-07
DOI
https://doi.org/10.30574/ijsra.2025.15.2.1279
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

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

ERWIN B. BERRY, PERFECTO L. ERNO
1 links
International Journal of Science and Research Archive
Online Learning and Analytics
article

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

ERWIN B. BERRY, PERFECTO L. ERNO
article en

Abstract

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.

International Journal of Science and Research ArchiveVol. 15(2)
Openalex Percentile: Top 91%
Online Learning and Analytics
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