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 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.

 

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 behaviors 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