Abstract
The integration of machine intelligence (MI) in education is transforming traditional pedagogies, offering innovative tools that can significantly enhance science education for secondary students. This conceptual paper explores the development and evaluation of MI-driven learning tools designed to improve student engagement, comprehension, and academic outcomes in science education. By examining the theoretical foundations of MI in education, reviewing current applications, and discussing the challenges and opportunities associated with this integration, the paper provides a comprehensive analysis of how machine intelligence can be harnessed to create more effective and personalized learning experiences. The paper concludes with recommendations for educators, policymakers, and researchers to support the successful implementation and assessment of MI in secondary science education.
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