DECODING MATHEMATICAL STRUGGLES AMONG 9TH, 10TH, AND INTERMEDIATE GIRL STUDENTS USING AI-BASED ANALYSIS

Abstract

This study investigates the challenges faced by female students in grades 9, 10, and intermediate levels in mathematics, a subject essential for academic and professional growth. Despite reforms aimed at gender equality, girls continue to underperform due to socio-cultural barriers, outdated pedagogy, emotional hurdles, and unequal access to quality instruction. The objective of this study is to expose the causes of female students’ disengagement from mathematics and recommend AI-based, data-driven interventions to improve outcomes. Using a quantitative framework enhanced with fuzzy logic and sentiment analysis, the study analyzes questionnaires, test scores, and emotional feedback from 200 female students in Pakistan. Results show a strong correlation between teacher engagement and student performance (r = 0.91), underscoring the critical role of instructional support. This work proves the potential of AI to reveal hidden learning patterns and reduce gender gaps. Future research should discover adaptive AI tutoring and evaluate long-term impacts.