Exploring Science & Mathematics Models through AI Technology to Advance High School Education Right
Year: 2025
Type: article
Abstract: Advancements in Artificial Intelligence (AI) have begun to redefine the educational landscape, offering new pathways for exploring complex scientific and mathematical concepts in high school curricula. This article examines the intersection of AI and STEM education, focusing on how AI-powered tools and models can transform the teaching and learning of science and mathematics at the secondary level. The study begins by framing the persistent challenges faced by high school educators—ranging from limited instructional time and student disengagement to disparities in access to laboratory resources and individualized support. Against this backdrop, the integration of AI-based modeling platforms, such as intelligent tutoring systems, virtual labs, and adaptive assessment engines, offers promising avenues for deepening student understanding and promoting inquiry-based learning. The article provides a structured review of AI applications that support interactive simulations, real-time feedback, automated reasoning, and personalized scaffolding in both mathematics and science domains. Case examples illustrate how machine learning algorithms enhance students’ problem-solving abilities in algebra, geometry, physics, and chemistry, while natural language processing facilitates complex scientific communication. The article also explores AI’s role in democratizing access to advanced learning content, supporting differentiated instruction, and improving learning outcomes for diverse student populations. Importantly, the discussion addresses ethical, pedagogical, and infrastructural considerations associated with AI adoption in education, including teacher preparedness, algorithmic transparency, and data privacy. Recommendations are offered for policymakers, educators, and technologists on effectively scaling AI-enhanced models within high school systems to foster future-ready competencies in STEM.
Authors: Vorsah, Rejoice Elikem, Tamakloe, Tobias Amewuga
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6968607291
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