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Artificial Intelligence and Academic Scholarship
Abstract
The chapter examines the position of Artificial Intelligence (AI) in scholarship and education, including the evolution from simple rule-based systems to advanced machine learning systems. It emphasizes aspects of AI, such as technology-driven solutions (deep learning) and natural language processing (NLP), and their emerging impact on data analysis, content streamlining, and peer review. The chapter outlines the ethical implications (that is, replacing human intelligence and biases in AI algorithms) of applying and integrating scholarship in education and intellectual property. The chapter discusses potential barriers to AI-related issues with data quality, interpretability, and access concerning researchers from universities with limited funding opportunities. Overall, the chapter argues that AI promotes interdisciplinary collaborative work for scholarship in the human disciplines and causes us to reconceptualize scholarship differently, but ethical implications must be carefully considered.
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