AI-Driven Educational Transformation in Secondary Schools: Leveraging Data Insights for Inclusive Learning Environments

In recent years, in the field of education, there has been a progressive trend towards teaching that is more personalised to students' characteristics and some models of prediction failure that are more accurate. In this sense, machine learning techniques have contributed to this realisation. This transformation has been significantly influenced by the integration of machine learning techniques, which have played a crucial role in harnessing data to enhance educational practices. This paper investigates the transformative potential of Artificial Intelligence (AI) within secondary education, focusing on the utilization of student assessment data and socioeconomic contextual information. The primary objective of this paper is to investigate the transformative potential of Artificial Intelligence (AI) within secondary education, with a specific focus on the utilization of student assessment data and socioeconomic contextual information. So, this paper explores the application of AI algorithms to create tailored learning pathways, adaptive support mechanisms, and targeted interventions that accommodate diverse student backgrounds. The integration of AI in secondary education is envisioned not only as a means to enhance academic outcomes but also as a tool to promote social equity and inclusivity. By leveraging data insights, educators can identify and respond to the unique needs of each student, fostering an environment where learning is optimized for individual growth. Furthermore, the paper scrutinizes the ethical considerations and challenges inherent in deploying AI systems in educational settings, emphasizing the pivotal role of equity, transparency, and data privacy in these implementations. This research aims to offer educators, policymakers, and stakeholder's insights into harnessing AI to foster adaptable, student-centric learning environments that bridge educational gaps and promote holistic academic development in secondary schools. Ethical guidelines and frameworks are discussed to ensure responsible AI deployment in educational contexts, safeguarding the rights and privacy of students. The data explored is taken from the management system of a secondary school in the municipality of Braga, relating to students taking Maths A, Maths B or Maths Applied to the Social Sciences (MACS). A total of 621 students were analysed, of which: 520 students attended Mathematics A in the Science and Technology and Socio-Economic Sciences courses, 20 attended Mathematics B in the Visual Arts course and 81 attended Mathematics Applied to Social Sciences in the Languages and Humanities course. The different maths subjects were analysed separately and at the end a comparative study was carried out between the three strands. Grounded in the analysis of these integrated datasets, the study sheds light on the pivotal role of AI in revolutionizing secondary school education. By closely examining student assessment data alongside socioeconomic indicators, such as academic performance and behavioral patterns, the paper identifies opportunities for AI integration to personalize learning experiences, address educational disparities, and cultivate inclusive learning environments. In Maths A there are 268 female and 252 male students. In Maths B there are 18 females and 2 males (most of the arts subjects are taken by women). In MACS the distribution is as follows: 45 female and 36 male. All these students, regardless of the area they chose at the start of secondary school, have in common the choice of Maths, however Maths A will be taken throughout secondary school, while the other two strands are only taken in the first two years of secondary school. Data fusuion techniques have achieved better performance in this type of study, since the models themselves combine the predictions of two or more base models.