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Full Description
Artificial intelligence (AI) opens new opportunities for STEM education in K-12, higher education, and professional education contexts. This book summarizes AI in education (AIED) with a particular focus on the research, practice, and technological paradigmatic shifts of AIED in recent years.
The 23 chapters in this edited collection track the paradigmatic shifts of AIED in STEM education, discussing how and why the paradigms have shifted, explaining how and in what ways AI techniques have ensured the shifts, and envisioning what directions next-generation AIED is heading in the new era. As a whole, the book illuminates the main paradigms of AI in STEM education, summarizes the AI-enhanced techniques and applications used to enable the paradigms, and discusses AI-enhanced teaching, learning, and design in STEM education. It provides an adapted educational policy so that practitioners can better facilitate the application of AI in STEM education.
This book is a must-read for researchers, educators, students, designers, and engineers who are interested in the opportunities and challenges of AI in STEM education.
Contents
Section I: AI-Enhanced Adaptive, Personalized Learning 1. Artificial intelligence in STEM education: current developments and future considerations 2. Towards a deeper understanding of K-12 students' CT and engineering design processes 3. Intelligent science stations bring AI tutoring into the physical world 4. Adaptive Support for Representational Competencies during Technology-Based Problem Solving in STEM 5. Teaching STEM subjects in non-STEM degrees: An adaptive learning model for teaching Statistics 6. Removing barriers in self-paced online learning through designing intelligent learning dashboards Section II: AI-Enhanced Adaptive Learning Resources 7. PASTEL: Evidence-based learning engineering methods to facilitate creation of adaptive online courseware 8. A Technology-Enhanced Approach for Locating Timely and Relevant News Articles for Context-Based Science Education 9. Adaptive learning profiles in the education domain Section III: AI-Supported Instructor Systems and Assessments for AI and STEM Education 10. Teacher orchestration systems supported by AI: Theoretical possibilities and practical considerations 11. The role of AI to support teacher learning and practice: A review and future directions 12. Learning outcome modeling in computer-based assessments for learning 13. Designing automated writing evaluation systems for ambitious instruction and classroom integration Section IV: Learning Analytics and Educational Data Mining in AI and STEM Education 14. Promoting STEM education through the use of learning analytics: A paradigm shift 15. Using learning analytics to understand students' discourse and behaviors in STEM education 16. Understanding the role of AI and learning analytics techniques in addressing task difficulties in STEM education 17. Learning analytics in a Web3D-based inquiry learning environment 18. On machine learning methods for propensity score matching and weighting in educational data mining applications 19. Situating AI (and Big Data) in the Learning Sciences: Moving toward large-scale learning sciences 20. Linking Natural Language Use and Science Performance Section V: Other Topics in AI and STEM Education 21. Quick Red Fox: An app supporting a new paradigm in qualitative research on AIED for STEM 22. A systematic review of AI applications in computer-supported collaborative learning in STEM education 23. Inclusion and equity as a paradigm shift for artificial intelligence in education
