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Navigating AI Biases in Education: A Foundation for Equitable Learning
Abstract
There is revolutionary potential when artificial intelligence (AI) is used into education. The use of AI in educational systems holds promise for individualized instruction and evaluation. It has, nevertheless, also made clear several serious biases and shortcomings. This chapter critically investigates the biases—such as cultural biases and socioeconomic disparities—that are ingrained in AI algorithms used in education. It looks at how inadequate AI is at meeting a range of student needs, including socioeconomic understanding and complicated emotional states. A framework is put forth to address these problems: data-driven diversity, ethical AI design, transparency and accountability, human-AI collaboration, and ongoing assessment and improvement. By putting these tactics into practice, stakeholders can minimize prejudices and fully utilize AI's promise, creating fair and productive learning environments for all students.
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