This study delved into the factors affecting secondary school students’ interest to learn Mathematics. The aim was to gather insights that can inform strategies aimed at enhancing students' engagement, enthusiasm, and achievement in Mathematics education. Literature information was downloaded using databases such as Google Scholar, ERIC, Search 4 Life, Scopus, Web of Science, and Academia. Of the 129 studies obtained, 117 articles were retained after removing duplicates and studies that did not meet the themes of the study. Further filtering of studies by removing primary and higher learning school-related studies allowed the retention of 25 relevant pieces of research published between 2000 and 2024. The results from the systematic reviews analysis showed that instructional strategy, instructional materials, the importance of Mathematics, a future career in Mathematics, students’ attitudes towards Mathematics, students’ enjoyment of Mathematics lessons, teachers and parental support, and students’ perception towards Mathematics, are amongst the key factors affecting positively secondary school students’ interest to learn Mathematics.  

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.

This study explores how mentor teachers in specialized teaching areas, particularly chemistry, interact in an Online Professional Development (OPD) program. The Mentor Teacher Professional Development (MeT-PD) program was designed to improve mentoring practices by creating opportunities for collaborative learning through various online activities, such as Individual Response (IR), Interactive Individual Response (IIR), Small Group Discussions (SGD), and Large Group Discussions (LGD). Using a qualitative case study approach, the research analyzed data collected from Zoom recordings and Nearpod activity logs. The findings indicate that while LGDs were useful for interactions between facilitators and learners, they were not as effective in fostering interaction among learners themselves, mainly due to the cognitive demands and how these discussions were structured. On the other hand, SGDs seemed to foster stronger participant interaction, probably because the smaller group settings led to more valuable exchanges. These findings highlight the need of thoughtful planning of OPD activities, with particular focus on group size management and selection of suitable discussion formats to improve both interaction and learning outcomes.

In this study, a topic-based analysis of students’ academic performance in mathematics across specific topic areas in selected lower secondary schools in Rwanda was conducted. The research examined third-term exam scripts (2022-2023 and 2023-2024) of 267 Senior One (S1) and Senior Two (S2) students at Groupe Scolaire Rushara, Groupe Scolaire Sheli, and Groupe Scolaire Nyarugugu, along with data from their eight mathematics teachers. A quantitative approach was employed using ANOVA to compare students’ performance across five key mathematics topics: algebra, metric measurement, proportional reasoning, geometry, and statistics and probability. The findings revealed significant improvement in statistics and probability (p =.000, η² = 0.293) and geometry (p =.000, η² = 0.178) between S1 and S2 students. In contrast, metric measurement showed no significant difference (p =.234, η² = 0.003), while algebra demonstrated minimal improvement (p =.050, η² = 0.007). Proportional reasoning showed moderate progress (p =.000, η² = 0.057), although students continued to struggle with applying proportional relationships. These results indicate that while notable gains were made in some areas, others require targeted pedagogical interventions to improve students’ conceptual understanding and performance in mathematics. The study underscores the importance of adaptive teaching strategies, enhanced instructional materials, and a more student-centered approach to mathematics education in lower secondary schools in Rwanda.

This study assessed the benefits and challenges of leveraging artificial intelligence in transforming science education in public universities in Kogi State, Nigeria. The population of this study comprises 52 science educators from the four public universities in Kogi State, Nigeria. There was no sampling since the population was manageable. The study adopted a descriptive survey research design. The instrument used for data collection was an online Google Form survey questionnaire titled Benefit and Challenges of Leveraging Artificial Intelligence Questionnaire (BCLAIQ). BCLAIQ contained 36 items and underwent trial testing. Cronbach’s alpha was used to analyze the reliability value, which yielded a value of .87. Three research questions and three null hypotheses guided the study. Mean and standard deviation scores were used to answer the research questions, while inferential statistics, specifically the t-test, were used to test the null hypotheses. The study revealed that there is no significant difference between the mean ratings of male and female respondents’ opinions on the benefits and challenges of leveraging artificial intelligence in transforming science education, respectively {t = 1.98, df =50, p > .05} {t = 1.83, df = 50, p > .05}. Thus, it was recommended, among other things, that government university administrators and relevant stakeholders should subsidize, partner with tech companies, and invest in AI-powered technologies. University administrators and relevant stakeholders should prioritize AI literacy and ethics by providing diverse professional staff training on AI fundamentals.