Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology

Artificial intelligence (AI) opens new opportunities for STEM education in K-12, higher education, and professional education contexts. This book summarises 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, 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 and summarises the AI-enhanced techniques and applications used to enable the paradigms.

Key Features

• Focus on AI in education with emphasis on recent paradigmatic shifts.
• Explores the integration of AI techniques in STEM education.
• Provides an adapted educational policy for practitioners.
• A comprehensive collection of 23 chapters by various authors.

Additional Information

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.

Specifications

Table of Contents

Section I: AI-Enhanced Adaptive, Personalized Learning

• 1. Artificial intelligence in STEM education: current developments and future considerations - Fan Ouyang, Pengcheng Jiao, Amir H. Alavi, Bruce M. McLaren
• 2. Towards a deeper understanding of K-12 students' CT and engineering design processes - Gautam Biswas, Nicole M Hutchins
• 3. Intelligent science stations bring AI tutoring into the physical world - Nesra Yannier, Scott E. Hudson, Kenneth R. Koedinger
• 4. Adaptive Support for Representational Competencies during Technology-Based Problem Solving in STEM - Martina A. Rau
• 5. Teaching STEM subjects in non-STEM degrees: An adaptive learning model for teaching Statistics - Daniela Pacella, Rosa Fabbricatore, Alfonso Iodice D’Enza, Carla Galluccio, Francesco Palumbo
• 6. Removing barriers in self-paced online learning through designing intelligent learning dashboards - Arta Faramand, Hongxin Yan, M. Ali Akber Dewan, Fuhua Lin

Section II: AI-Enhanced Adaptive Learning Resources

• 7. PASTEL: Evidence-based learning engineering methods to facilitate creation of adaptive online courseware - Noboru Matsuda, Machi Shimmei, Prithviraj Chaudhuri, Dheeraj Makam, Raj Shrivastava, Jesse Wood, Peeyush Taneja
• 8. A Technology-Enhanced Approach for Locating Timely and Relevant News Articles for Context-Based Science Education - Jinnie Shin, Mark J. Gierl
• 9. Adaptive learning profiles in the education domain - Claudio Giovanni Demartini, Andrea Bosso, Giacomo Ciccarelli, Lorenzo Benussi, Flavio Renga

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 - Suraj Uttamchandani, Haesol Bae, Chen (Carrie) Feng, Krista Glazewski, Cindy E. Hmelo-Silver, Thomas Brush, Bradford Mott, James Lester
• 11. The role of AI to support teacher learning and practice: A review and future directions - Jennifer L. Chiu, James P. Bywater, Sarah Lilly
• 12. Learning outcome modeling in computer-based assessments for learning - Fu Chen, Chang Lu
• 13. Designing automated writing evaluation systems for ambitious instruction and classroom integration - Lindsay Clare Matsumura, Elaine L. Wang, Richard Correnti, Diane Litman

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 - Shan Li, Susanne P. Lajoie
• 15. Using learning analytics to understand students’ discourse and behaviours in STEM education - Gaoxia Zhu, Wanli Xing, Vitaliy Popov, Yaoran Li, Charles Xie, Paul Horwitz
• 16. Understanding the role of AI and learning analytics techniques in addressing task difficulties in STEM education - Sadia Nawaz, Emad A. Alghamdi, Namrata Srivastava, Jason Lodge, Linda Corrin
• 17. Learning analytics in a Web3D-based inquiry learning environment - Guangtao Xu
• 18. On machine learning methods for propensity score matching and weighting in educational data mining applications - Juanjuan Fan, Joshua Beemer, Xi Yan, Richard A. Levine
• 19. Situating AI (and Big Data) in the Learning Sciences: Moving toward large-scale learning sciences - Danielle S. McNamara, Tracy Arner, Reese Butterfuss, Debshila Basu Mallick, Andrew S. Lan, Rod D. Roscoe, Henry L. Roediger III, Richard G. Baraniuk
• 20. Linking Natural Language Use and Science Performance - Scott Crossley, Danielle S. McNamara, Jennifer Dalsen, Craig G Anderson, Constance Steinkuehler

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 - Stephen Hutt, Ryan S. Baker, Jaclyn Ocumpaugh, Anabil Munshi, J.M.A.L. Andres, Shamya Karumbaiah, Stefan Slater, Gautam Biswas, Luc Paquette, Nigel Bosch, Martin van Velsen
• 22. A systematic review of AI applications in computer-supported collaborative learning in STEM education - Jingwan Tang, Xiaofei Zhou, Xiaoyu Wan, Fan Ouyang
• 23. Inclusion and equity as a paradigm shift for artificial intelligence in education - Rod D. Roscoe, Shima Salehi, Nia Dowell, Marcelo Worsley, Chris Piech, Rose Luckin