Gender-Based Analysis of Generative AI’s Effectiveness in Enhancing Algebra Achievement in Senior Secondary Schools in Funtua Educational Zone, Katsina State.
Keywords:
Generative Artificial Intelligence (GenAI), Algebra, Academic Performance, GenderAbstract
This study explored the impact of Generative Artificial Intelligence (GenAI) on algebra performance among male and female senior secondary school students in the Funtua Educational Zone, Katsina State, Nigeria. A quasi-experimental design with a one-group pretest-posttest approach was employed. The study targeted 5,245 SS2 Mathematics students across 22 public secondary schools in the zone. A total of 61 students were randomly selected from one co-educational school among the 18 available. The experimental group, comprising both male and female students, received Algebra instruction using GenAI-based teaching methods. Data collection was conducted using the Algebra Performance Test (APT), which was validated by experts from Ahmadu Bello University, Zaria, and achieved a reliability coefficient of 0.69 through Pearson Product-Moment Correlation (PPMC). The study was guided by one research question and one null hypothesis. The research question was analyzed using Mean, Standard Deviation, and Mean Difference statistics, while the hypothesis was tested using a Paired Sample t-test at a 0.05 significance level. Results indicated no significant gender-related differences in algebra performance within the experimental group. The study concludes that GenAI is an effective and inclusive instructional tool for enhancing algebra learning among secondary school students. It is recommended that Mathematics teachers incorporate Generative Artificial Intelligence into their teaching strategies to enhance students’ comprehension of complex mathematical concepts such as Algebra.
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