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Artificial Intelligence-Based STEM Education

Xiaoming Zhai, Joseph Krajcik

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Abstract

Abstract This chapter provides a comprehensive exploration of the integration of artificial intelligence (AI) into science, technology, engineering, and mathematics (STEM) education, elucidating both its transformative potential and associated challenges. The chapter critically examines the findings in the book, including the roles of AI in personalized learning, assessment automation, AI tool design and development, and teacher professional development while also addressing ethical and inclusivity concerns. It provides a set of key recommendations for future research, highlighting the need for empirical studies that focus on diverse learner populations, validity, pedagogy, and ethical frameworks to guide AI's responsible deployment. The chapter serves as a seminal resource for educators, researchers, and policymakers, offering a nuanced understanding of the complexities involved in leveraging AI to enhance STEM education. It concludes by outlining key future directions, emphasizing the need for interdisciplinary efforts to realize the full potential of AI-based STEM education.

Topics & Concepts

Transformative learningEngineering ethicsSoftware deploymentSet (abstract data type)PedagogyKnowledge managementEngineeringPsychologyComputer scienceSoftware engineeringProgramming languageOnline Learning and Analytics