Effect of Artificial Intelligence on Secondary School Students' Academic Performance in Ika South LGA, Delta State
Abstract This work looked at the impact of Artificial Intelligence (AI) on the academic performance of secondary school students in Ika South Local Government Area, Delta State, Nigeria. The research was guided by two questions and tested two hypotheses at a 0.05 level of significance. A correlational research design was employed. The study population consisted of 5,250 Senior Secondary School 2 (SS2) students in Ika South L.G.A. for the 2024/2025 academic session. A two-stage multi-sampling technique was utilized: in the first stage, 10 public secondary schools were selected from both urban and rural areas of the L.G.A.; in the second stage, 200 students were chosen from the selected schools. Three research tools were used for data collection: the Questionnaire on AI and Learning Abilities of Students (QAILAS), the Questionnaire on AI and Problem-Solving Skills of Students (QAIPSS), and the Questionnaire on AI and Critical Thinking of Students (QAICTS). These instruments were face-validated by three education lecturers in the Faculty of Education, University of Delta, Agbor. To test the reliability of the tools, they were administered to 30 students outside the selected schools but within the same academic level and environment. A test-retest procedure was conducted, and the results were analyzed using Pearson Product-Moment Correlation, yielding a reliability coefficient of 0.61 at the 0.05 significance level. This confirmed the instruments' reliability and appropriateness for the study. Mean and Standard Deviation were used to analyze data, frequency tables for research questions and Pearson correlation was used to test the hypotheses. The findings revealed a meaningful relationship between AI tools and students' level of assimilation. Based on these results, the study recommended that schools integrate AI tools into teaching practices to enhance students' learning capabilities and critical thinking skills.Top of Form
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-06-01
- DOI
- https://doi.org/10.5281/zenodo.17073151
- Primary Topic
- Geochemistry and Geologic Mapping
- Type
- article
- Field-Weighted Citation Impact
- 0.00