MOBILE LEARNING, SELF-ESTEEM AND PERCEIVED ACADEMIC PERFORMANCE IN MATHEMATICS AMONG SENIOR SECONDARY SCHOOL STUDENTS IN LAGOS, NIGERIA: IMPLICATIONS FOR AI-ENABLED EDUCATIONAL TOOLS

Abstract

The growing use of smartphones among adolescents, together with the increasing incorporation of artificial intelligence (AI) features into educational applications, has renewed interest in the role of mobile learning (m-learning) in students' self-esteem and academic performance. This study examined the association between mobile learning, self-esteem and perceived mathematics performance among senior secondary school students at Yaba College of Technology Staff School, Lagos, Nigeria, and discussed the findings in relation to the emerging AI-enabled mobile-learning ecosystem. A descriptive cross-sectional survey was conducted with 100 Senior Secondary I–III students selected through random sampling. Data were collected using the Rosenberg Self-Esteem Scale and a researcher-designed mobile-learning questionnaire and analysed using descriptive statistics and exploratory chi-square goodness-of-fit tests. Sixty-nine percent of respondents reported owning a smart mobile device, although only 31% considered such devices affordable. Most respondents reported positive changes in self-worth (81%), self-confidence (80%) and perceived competence (87%), while 71–84% reported perceived improvements across mathematics-performance indicators. Between 67% and 75% also rated mobile learning more favourably than traditional classroom instruction across the measured dimensions. The chi-square results indicated that response distributions differed significantly from an equal four-category distribution (p < .01); these tests should not be interpreted as evidence of causal effects. Because the instrument did not distinguish AI-enabled applications from generic mobile-learning tools, the study provides baseline evidence about mobile learning rather than direct evidence of AI-specific effects. The findings highlight the need for equitable access, stronger measurement and future studies that explicitly identify AI-enabled tools and use objective academic outcomes.