This study investigated the integration of artificial intelligence (AI) tools into secondary school chemistry education in Zimbabwe, assessing their impact on student engagement and academic performance. Grounded in Vygotsky’s Sociocultural Theory and Cognitive Load Theory, the research employed a mixed-methods approach within a pragmatic framework. Quantitative data were collected through pre-test and post-test assessments and structured surveys, comparing an experimental group using AI tools with a control group employing traditional methods. Qualitative data from student and teacher interviews and classroom observations were analysed thematically. ANCOVA analysis revealed a statistically significant difference in post-test scores between the experimental and control groups, F (1, 117) = 188.86, p < .005, η² = 0.617, demonstrating a large effect size of AI integration on academic performance. Students in the experimental group exhibited a mean improvement of 20%, controlling for pre-test differences. Additionally, interaction effects between AI use and gender (F (1,115) = 0.17, p = .684) as well as prior chemistry knowledge (F (1,115) = 0.05, p = .829) were not statistically significant. Furthermore, 85% of the experimental group reported higher engagement levels, confirming AI’s role in fostering motivation and conceptual understanding. AI tools facilitated personalized learning paths, interactive simulations, and real-time feedback, optimizing cognitive efficiency and deep learning. Despite these advantages, significant challenges emerged, including limited internet access, insufficient technological resources, lack of teacher training, and curriculum integration difficulties. These barriers highlight the need for strategic investments in digital infrastructure, professional development for educators, and curriculum revisions to fully integrate AI into chemistry education. The findings underscore AI’s transformative potential in STEM education within developing nations. Addressing infrastructural and pedagogical challenges is critical to maximizing AI's impact, ensuring equitable access, and fostering long-term sustainability in educational innovation.

Global concern surrounds students' mathematics learning, development, and achievement. Scholarly discussions have explored various factors influencing students' mathematics performance. However, more information is needed to understand the impact of mathematics teaching styles on student outcomes in developing contexts like Nepal. This study examines the moderators of mathematics teaching styles and their influence on students' performance. To achieve this, the Teachers' Teaching Style Questionnaire (TTSQ) collected quantitative data from 469 grade nine students across 14 high schools in Kathmandu, Lalitpur, and Bhaktapur districts of Nepal. Confirmatory factor analysis, path analysis, and moderation analysis were performed to examine the effects of teaching styles on student achievement in mathematics. Key findings indicate that teaching styles, such as consideration and openness, are not significant predictors of student achievement, but rigid teaching styles can predict student achievement in mathematics. However, impact of the rigid teaching style was negative on student achievement. School type influenced the relationship between performance and considerate teaching, favoring private schools. School location influenced the relationship between considerate teaching and student performance in mathematics, favoring rural schools. Likewise, urban schools had a negative effect on the relationship between teacher openness and student performance, but rural schools had a positive effect on their relationship. Furthermore, low and high-ability students moderated the relationship between considerate teaching and student achievement, with the negative effect of low ability on considerate teaching and student performance and the positive influence of high ability on considerate teaching and student achievement. Student ability influenced the relationship between teacher openness and student performance, with a negative moderations of low and moderate ability students. The study concludes by emphasizing the importance of teacher training in teaching styles for high schools in Nepal and similar contexts.

This study examines the validity of the Force Concept Inventory (FCI) in Ugandan secondary schools using Item Response Curves (IRCs) and provides a comparative evaluation of its effectiveness across different educational contexts. The survey focused on Senior Four students preparing for the Uganda Certificate of Education (UCE) examinations, with a representative sample of 941 students (aged 15–17) selected through a multi-stage sampling technique. The initial analysis employed Classical Test Theory (CTT) metrics before the detailed analysis of IRCs for the FCI items. The CTT evaluates item-level and whole test statistics like item difficulty level, discrimination index, and reliability. The CTT indices revealed that the FCI was highly challenging, with an average score of 5.76 out of 30 and a low-reliability coefficient (α = 0.15). Additionally, 73.3% of the items showed poor discrimination, and some distractors were ineffective. The detailed analysis of IRCs showed that several FCI items are inefficient in the context of the Ugandan education system. The IRCs also demonstrated a widespread choice of distractors for many items, with overall scores falling below the threshold indicative of a generally agreed-upon understanding of Newtonian physics. Comparative analysis from other global contexts studies suggests that language barriers, curriculum differences, and instructional methods influence student performance. These findings underscore the necessity of adapting the FCI tool to better fit local educational contexts and implementing additional instructional strategies to enhance conceptual understanding. A more culturally and contextually adapted diagnostic tool may improve physics education and better assess students’ conceptual comprehension of force and motion within the region.