Artificial Intelligence in Science Education: An Integrative Review of Personalized Learning, Ethics, and Policy Challenges

Artificial intelligence (AI) is swiftly revolutionizing science education through the implementation of adaptive and generative technologies that personalize learning, automate assessment, and facilitate inquiry-based experimentation. This integrative review consolidates empirical and conceptual works published from 2019 to 2024 about AI applications in physics and allied sciences, analyzing their educational potential, professional development needs, and ethical considerations. Studies indicate that intelligent tutoring systems, predictive analytics, and natural language processing technologies can improve conceptual comprehension, offer immediate feedback, and facilitate differentiated training on a large scale. These advantages, however, rely on continuous teacher professional development that enhances AI literacy, promotes critical analysis of algorithmic results, and facilitates the creation of hybrid learning environments that integrate AI with existing digital resources. The analysis highlights critical obstacles, such as algorithmic bias, data privacy issues, openness in decision-making, and enduring inequities in infrastructure that threaten to exacerbate the digital divide. Resolving these concerns necessitates synchronized policy interventions, strong regulatory structures, and inclusive professional learning communities to guarantee that AI functions as a collaborative ally rather than a replacement for educators. This article elucidates the opportunities and systemic risks associated with AI adoption, offering guidelines for educators, researchers, and policymakers aiming to integrate AI in a manner that reinforces the fundamental ideals of science education: curiosity, equity, and critical inquiry.