<![CDATA[Particularly in science and math classes where students struggle with abstract thinking, conceptual comprehension, problem-solving, and sustained academic engagement, Artificial Intelligence (AI) has grown in importance as a component of instructional technology. As a result, AI-enabled digital solutions provide chances for data-driven teaching support, adaptive feedback, and personalized learning pathways. With a focus on conceptual comprehension, problem-solving skills, academic engagement, and self-directed learning, this research sought to determine the degree to which AI-powered digital tools improve students’ learning outcomes in science and mathematics. The study used a mixed-methods approach, combining qualitative information from student comments and classroom observations with quantitative assessments of academic progress. The research was conducted in upper secondary and postsecondary educational environments. Science and math classrooms are using AI-supported technology like data-driven instructional dashboards, intelligent feedback mechanisms, and adaptive learning systems. While qualitative data offered insight into student participation, classroom interaction, and perceived learning assistance, quantitative data were used to evaluate students’ learning performance before and after the intervention. The results show that using AI-supported digital tools enhanced learning outcomes, especially in courses that call for gradual conceptual expansion, abstract cognition, and iterative reasoning. Additionally, qualitative data indicated that students benefited from increased autonomy during learning activities, timely feedback, ongoing observation, and personalized learning paths. The research comes to the conclusion that, when paired with curriculum goals and efficient instructional design, AI-supported digital tools should be seen as cognitive and pedagogical support systems rather than just technical advancements that might enhance science and math teaching.]]>
