Uses of Artificial Intelligence in STEM EducationXiaoming Zhai, Joseph Krajcik In the age of rapid technological advancements, the integration of Artificial Intelligence (AI), machine learning (ML), and large language models (LLMs) in Science, Technology, Engineering, and Mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. Uses of AI in STEM Education, comprising 25 chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools. As the chapters unfold, readers learn about innovative AI applications, from automated scoring systems in biology, chemistry, physics, mathematics, and engineering to intelligent tutors and adaptive learning. The book also touches upon the nuances of AI in supporting diverse learners, including students with learning disabilities, and the ethical considerations surrounding AI's growing influence in educational settings. It showcases the transformative potential of AI in reshaping STEM education, emphasizing the need for adaptive pedagogical strategies that cater to diverse learning needs in an AI-centric world. The chapters further delve into the practical applications of AI, from scoring teacher observations and analyzing classroom videos using neural networks to the broader implications of AI for STEM assessment practices. Concluding with reflections on the new paradigm of AI-based STEM education, this book serves as a comprehensive guide for educators, researchers, and policymakers, offering insights into the future of STEM education in an AI-driven world. |
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accuracy AI-based algorithms analysis Annotator application Artificial Intelligence automated feedback automated scoring automatic challenges classroom cognitive Cohen's kappa concepts constructed response contexts Data Mining dataset discourse domain-specific model Dyslexia Education and Technology Educational Research ethical evaluation evidence example explanation Figure framework Gobert guidance Haudek human hyperparameters identify impact inquiry instructional activities integration intelligent tutoring systems interaction Journal of Research Journal of Science knowledge Krajcik labels learners Learning Analytics learning disabilities Linn logistic regression Machine Learning Mathematics natural language processing Nehm neural networks NGSS pedagogical Pellegrino performance potential practices prediction random forest Research in Science Review revision rubric scaffolding Science Assessment Science Education Science Teaching scientific argumentation simulation social robots STEM education strategies subject competency support students SWLDs talk moves tasks teachers testing tion topic understanding users validity