Handbook of Artificial Intelligence in Education


Handbook of Artificial Intelligence in Education

9781800375406 Edward Elgar Publishing
Edited by Benedict du Boulay, Emeritus Professor of Artificial Intelligence, University of Sussex, UK, Antonija Mitrovic, Professor, Department of Computer Science and Software Engineering, University of Canterbury, New Zealand and Kalina Yacef, Associate Professor of Computer Science, University of Sydney, Australia
Publication Date: 2023 ISBN: 978 1 80037 540 6 Extent: 696 pp
Gathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day.

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Gathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day.

The Handbook evaluates the use of AI techniques such as modelling in closed and open domains, machine learning, analytics, language understanding and production to create systems aimed at helping learners, teachers, and educational administrators. Chapters examine theories of affect, metacognition and pedagogy applied in AIED systems; foundational aspects of AIED architecture, design, authoring and evaluation; and collaborative learning, the use of games and psychomotor learning. It concludes with a critical discussion of the wider context of Artificial Intelligence in Education, examining its commercialisation, social and political role, and the ethics of its systems, as well as reviewing the possible challenges and opportunities for AIED in the next 20 years.

Providing a broad yet detailed account of the current field of Artificial Intelligence in Education, researchers and advanced students of education technology, innovation policy, and university management will benefit from this thought-provoking Handbook. Chapters will also be useful to support undergraduate courses in AI, computer science, and education.
Critical Acclaim
‘The Handbook of Artificial Intelligence in Education is a great resource for people studying the field of AI and data-directed education, which is a complex tapestry, with many different elements feeding into its design and development. The Handbook supports readers to experience the full tapestry and to pull out and examine individual threads, without losing the underlying purpose. The field has grown tremendously in the past 30 years. Intelligent tutors now listen to and speak to learners, model student expertise and knowledge, examine theories (about how humans learn, think, collaborate and socialize), and support classroom orchestration, learning at scale, assessment, and human-machine interaction. The Handbook nicely documents progress in the field without overwhelming a new generation of AIED researchers.’
– Beverly Woolf, author of Building Intelligent Interactive Tutors and University of Massachusetts, Amherst, US

‘This is a really great Handbook written by some of the most well known authors in the field of Artificial Intelligence in Education (AIED). It describes both the most important topics and future trends in a very comprehensive way for a wide range of stakeholders.’
– Cristóbal Romero, University of Córdoba, Spain
Contributors: Vincent Aleven, Ivon Arroyo, Roger Azevedo, Nicole Barbaro, Tiffany Barnes, Gautam Biswas, Stephen Blessing, Christopher Brooks, Peter Brusilovsky, Alberto Casas-Ortiz, Min Chi, Cristina Conati, Mutlu Cukurova, Huw C. Davies, Vania Dimitrova, Benedict du Boulay, Jon Echeverria, Rebecca Eynon, Ying Fang, Claude Frasson, Dragan Gašević, Stephen Gilbert, Arthur Graesser, Neil Heffernan, Wayne Holmes, Ken Holstein, Yun Huang, Judy Kay, Hassan Khosravi, Jeremy Knox, Kenneth R. Koedinger, Vitomir Kovanović, Bob Kummerfeld, Susanne Lajoie, Sebastien Lalle, H. Chad Lane, James Lester, Shan Li, Rosemary Luckin, Ye Mao, Roberto Martinez-Maldonado, Samiha Marwan, Manolis Mavrikis, Gordon McCalla, Bruce McLaren, Danielle S. McNamara, Antonija Mitrovic, Riichiro Mizoguchi, Jack Mostow, Kasia Muldner, Selena Nemorin, Huy A. Nguyen, Quan Nguyen, Stellan Ohlsson, Andrew M. Olney, Jennifer Olsen, Kaśka Porayska-Pomsta, Stanislav Pozdniakov, Thomas W. Price, Ethan Prihar, Meredith Riggs, Steven Ritter, Maria Mercedes T. Rodrigo, Rod D. Roscoe, Carolyn Rosé, Jonathan Rowe, Nikol Rummel, Vasile Rus, Olga C. Santos, Preya Shabrina, Yang Shi, Shaveen Singh, Sergey Sosnovsky, Zachari Swiecki, Anouschka van Leeuwen, Kurt VanLehn, Julita Vassileva, Megan Wiedbusch, Ben Williamson, Kalina Yacef

Foreword xii

1 Introduction 2
Benedict du Boulay, Antonija Mitrovic and Kalina Yacef
2 The history of artificial intelligence in education – the first quarter century 10
Gordon McCalla

3 The role and function of theories in AIED 31
Stellan Ohlsson
4 Theories of metacognition and pedagogy applied to AIED systems 45
Roger Azevedo and Megan Wiedbusch
5 Theories of affect, meta-affect, and affective pedagogy 68
Ivon Arroyo, Kaśka Porayska-Pomsta and Kasia Muldner
6 Scrutable AIED 101
Judy Kay, Bob Kummerfeld, Cristina Conati, Kaśka Porayska-Pomsta and Ken Holstein

7 Domain modeling for AIED systems with connections to modeling student knowledge: a review 127
Vincent Aleven, Jonathan Rowe, Yun Huang and Antonija Mitrovic
8 Student modeling in open-ended learning environments 170
Cristina Conati and Sébastien Lallé
9 Six instructional approaches supported in AIED systems 184
Vincent Aleven, Manolis Mavrikis, Bruce M. McLaren, Huy A. Nguyen, Jennifer Olsen and Nikol Rummel
10 Theory-driven design of AIED systems for enhanced interaction and problem-solving 229
Susanne Lajoie and Shan Li
11 Deeper learning through interactions with students in natural language 250
Vasile Rus, Andrew M. Olney and Arthur C. Graesser
12 Authoring tools to build AIED systems 273
Stephen Blessing, Stephen B. Gilbert and Steven Ritter

13 Continuous student modeling for programming in the classroom: challenges, methods, and evaluation 287
Ye Mao, Samiha Marwan, Preya Shabrina, Yang Shi, Thomas W. Price, Min Chi and Tiffany Barnes
14 Human–AI co-orchestration: the role of artificial intelligence in orchestration 309
Ken Holstein and Jennifer Olsen
15 Using learning analytics to support teachers 322
Stanislav Pozdniakov, Roberto Martinez-Maldonado, Shaveen Singh, Hassan Khosravi and Dragan Gašević
16 Predictive modeling of student success 350
Christopher Brooks, Vitomir Kovanović and Quan Nguyen
17 Social analytics to support engagement with learning communities 370
Carolyn Rosé, Meredith Riggs and Nicole Barbaro

18 Intelligent systems for psychomotor learning: A systematic review and two cases of study 390
Alberto Casas-Ortiz, Jon Echeverria and Olga C. Santos
19 Artificial intelligence techniques for supporting face-to-face and online collaborative learning 422
Roberto Martinez-Maldonado, Anouschka van Leeuwen and Zachari Swiecki
20 Digital learning games in artificial intelligence in education (AIED): a review 440
Bruce M. McLaren and Huy A. Nguyen
21 Artificial intelligence-based assessment in education 487
Ying Fang, Rod D. Roscoe and Danielle S. McNamara
22 Evaluations with AIEd systems 507
Kurt VanLehn
23 Large-scale commercialization of AI in school-based environments 526
Steven Ritter and Kenneth R. Koedinger
24 Small-scale commercialisation: the golden triangle of AI EdTech 539
Rosemary Luckin and Mutlu Cukurova
25 Critical perspectives on AI in education: political economy, discrimination, commercialization, governance and ethics 555
Ben Williamson, Rebecca Eynon, Jeremy Knox and Huw Davies
26 The ethics of AI in education 573
Kaśka Porayska-Pomsta, Wayne Holmes and Selena Nemorin

27 The great challenges and opportunities of the next 20 years 608
1. AIED and equity 608
Maria Mercedes T. Rodrigo
2. Engaging learners in the age of information overload 610
Julita Vassileva
3. Pedagogical agents for all: designing virtual characters for inclusion and diversity in STEM 613
H. Chad Lane
4. Intelligent textbooks 616
Peter Brusilovsky and Sergey Sosnovsky
5. AI-empowered open-ended learning environments in STEM domains 620
Gautam Biswas
6. Ubiquitous-AIED: pervasive AI learning technologies 626
James C. Lester
7. Culture, ontology and learner modeling 629
Riichiro Mizoguchi
8. Crowdsourcing paves the way for personalized learning 632
Ethan Prihar and Neil Heffernan
9. AIED in developing countries: breaking seven WEIRD assumptions in the global learning XPRIZE field study 635
Jack Mostow
10. The future of learning assessment 639
Claude Frasson
11. Intelligent mentoring systems: tapping into AI to deliver the next generation of digital learning 642
Vania Dimitrova

Index 653
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