A review of AI-driven pedagogical strategies for equitable access to science education

Abstract

Access to quality science education is essential for equitable development and advancement in society. However, disparities in access to science education persist, particularly among marginalized and underserved populations. Artificial intelligence (AI) offers innovative solutions to address these disparities by enhancing pedagogical strategies that promote equitable access to science education. This review examines AI-driven pedagogical strategies aimed at improving equitable access to science education. The review explores how AI technologies, such as machine learning, natural language processing, and computer vision, can be leveraged to personalize learning experiences, provide real-time feedback, and enhance engagement among students from diverse backgrounds.AI-driven personalized learning platforms can adapt to individual learning styles and pace, ensuring that each student receives tailored instruction. These platforms can also provide additional support to students facing learning challenges, thus promoting inclusivity and equity in science education. Furthermore, AI-driven assessment tools can provide educators with insights into student performance and comprehension, enabling them to identify areas for improvement and provide targeted interventions. Additionally, AI can facilitate collaborative learning environments, allowing students to work together irrespective of their physical location, thus breaking down geographical barriers to access. However, the implementation of AI-driven pedagogical strategies raises ethical considerations, such as data privacy and algorithmic bias, which must be carefully addressed to ensure equitable access to science education for all students. In conclusion, AI-driven pedagogical strategies have the potential to revolutionize science education by enhancing personalized learning, providing real-time feedback, and fostering inclusive learning environments. However, careful consideration must be given to the ethical implications of AI implementation to ensure that these technologies are used responsibly and equitably.