When countries closed down schools in early 2020 to deal with the Covid-19 pandemic, learning went digital. In the year since, teachers, students and administrators have done, what is effectively, a collective crash course on digital education. There have been many serious downsides to it, from screen fatigue and adaptation stress to the falling behind of those ill-equipped for digital learning or unprepared to learn on their own. But the experience has catapulted education systems, traditionally laggards when it comes to innovation, years ahead in what would have been a slow slouch towards smart schooling.

Digitalisation opens up new possibilities for education. While education has always been rich in data such as grades or administrative information on students’ absenteeism, the use of data to help students learn better and teachers to teach better, and to inform decision-making in educational administrations is recent. Education stakeholders have had a difficult relationship with technology, alternating between strong enthusiasm and scepticism. Might digital technology, and, notably, smart, technologies based on artificial intelligence (AI), learning analytics, robotics, and others, transform education in the same way they are transforming the rest of society? If so, how might this look? This book explores this question.

This chapter serves as an introduction to the book and presents some of its findings and policy implications. After highlighting the importance of digitalisation as a societal trend for education, it introduces the main focus of the book: exploring the frontiers of education technology. Artificial intelligence and learning analytics are transforming (or have the potential to transform) educational practices, and so have other smart or advanced technologies such as robotics and blockchain. How can they improve classroom instruction and the management of educational establishments and systems? After presenting the objectives and chapters of the book, the chapter highlights the opportunities of smart technologies for education systems and points to some emerging policy issues and dimensions to consider before making some forward-looking concluding remarks.

Artificial intelligence has led to a generation of technologies in education – for use in classrooms and by school systems more broadly – with considerable potential to bring education forward. This chapter provides a broad overview of the technologies currently being used, their core applications, and their potential going forward. The chapter also provides definitions of some of the key terms that will be used throughout this book. It concludes with a discussion of the potentials that may be achieved if these technologies are integrated, the shifts in thinking about supporting learners through one-on-one learning experiences to influencing systems more broadly, and other key directions for R&D and policy in the future.

This chapter outlines the research and development of personalised learning in research labs and schools across OECD countries. The state of the art of personalised learning is described using a model of 6 levels of automation of personalised learning that articulates the roles of AI, teachers and learners. This describes how hybrid human-AI solutions combine the strengths of human and artificial intelligence to accomplish personalised learning. Existing learning technologies have a strong focus on diagnosing students’ knowledge and adjusting feedback, tasks and/or the curriculum. Developmental frontiers lie in taking into account a broader range of learner characteristics such as self-regulation, motivation and emotion.

Engagement is critical to learning, but it has proved challenging to promote both meaningful engagement and deep learning. Can digital learning technologies help? This chapter provides a broad overview of some promising paths to measure students’ level of engagement during learning with digital technologies and how these technologies can be designed to improve engagement on the onset of learning or when disengagement sets in. It discusses why engagement matters for learning, how to measure engagement with digital learning technologies, and presents different types of approaches in using data and technology to improve students’ engagement and learning.

The term “learning technologies” usually refers to the activities that one or a few learners perform on a digital device. How do technologies take into account the fact that a classroom has many more learners? Classroom analytics provide teachers with real-time support for class management: monitoring the engagement of learners in a class; deciding when and how to intervene in their learning activities; reusing the output from one activity in another one; forming student groups; integrating the learner’s production in a lecture; deciding when to shift to another activity; or helping teachers to regulate their own behaviour. This chapter describes the classroom itself as a digital system. It speculates on how this vision can come true, and what can already be learned from it, namely the critical role of teachers in the success of digital education.

This chapter explores the role of technology in supporting students with special needs. The support ranges from helping disabled students to access the curriculum to providing disability specific support so that students can participate in inclusive school settings. After highlighting the importance of supporting students with special needs, the chapter shows how technology can support a variety of special needs. It then focusses on three cutting-edge technologies which aim to: 1) support the development of autistic children’s social skills, 2) diagnose and support students with dysgraphia and 3) provide access to graphical materials for blind and visually impaired students. The examples highlight the importance of involving students and stakeholders in the design of the solutions and the need for developers to consider affordability as a key element of their development.

Robots in education largely fall into two categories: robots that are used to teach and enthuse children about STEM subjects, and the more recent application of robots as teachers. While the pedagogical potential of robots for STEM education has been extensively explored since the 1970s, robot teachers form a new technology, driven by new developments in artificial intelligence and robotics, which is currently the subject of research and proof-of-concept trials. These robots assist teachers in their pedagogical task by offering specific tutoring experiences to students. Their potential stems mainly from their ability to provide one-to-one tutoring and a physical presence, with the latter missing in traditional computer-based learning. While there are no commercial solutions yet aimed at formal education, research suggests that social robots do offer benefits which computer-based solutions do not. Their physical nature lends them to real-world interactions with learners, and they have an increased social presence, which enhances learning outcomes. There are, however, considerable technical, economical and logistical challenges to rolling out social robots in classrooms.

Learning analytics for educational organisations has been a topic of discussion for the past decade. Yet, there are few examples of organisation-wide systematic and holistic adoptions of learning analytics. This chapter explores actionable frameworks and adopting models that could help successfully integrate learning analytics into educational organisations in an organisational change approach. While higher education organisations are aware of learning analytics and have started experimenting with dashboards for students and teachers this is far from organisational transformation. Research on the implementation and practice of learning analytics in K-12 schools is also scarce.A look at adoption models and policy recommendations in a broader international context may help push isolated experiments with learning analytics into the mainstream.

This chapter discusses the literature and practice on emerging technologies to predict and prevent dropping out of upper secondary school. First, it presents the current research on early warning systems and early warning indicators and discusses the accuracy of predictors of dropout. It shows the value of such research, including a typology of dropout profiles. Second, it provides an overview of current emerging digital methodologies from pattern analytics, data science, big data analytics, learning analytics, and machine learning as applied to identifying accurate predictors of dropping out. The conclusion looks to the future of early warning systems and indicators, both from a research and policy perspective, calling for the need for open access algorithms and code for early warning systems and indicators research and practice, and for the inclusion of the community in their design and application, proposing a framework of “Four A’s of Early Warning Indicators” so that they are Accurate, Accessible, Actionable and Accountable.

This chapter discusses how recent advancements in digital technology could lead to a new generation of game-based standardised assessments in education, providing education systems with assessments that can test more complex skills than traditional standardised tests can. After highlighting some of the advantages of game-based standardised assessment compared to traditional ones, this chapter discusses how these tests are built, how they work, but also some of their limitations. While games have strong potential to improve the quality of testing and expand assessment to complex skills in the future, they will likely supplement traditional tests, which also have their advantages. Three examples of game-based assessments integrating a range of advanced technologies illustrate this perspective.

Blockchain technology is revolutionising the world of financial services by providing distributed networks for transacting digital currencies. This same digital infrastructure can be used to verify important claims and credentials, including educational and academic records. Within education, significant momentum exists worldwide to use blockchain technology for issuing, sharing, and verifying educational experiences and qualifications. This chapter provides an overview of blockchain technology and spotlights its use in education to create portable, interoperable, user-controlled digital credentials. These verifiable claims constitute a form of social currency that empowers students and workers with the ability to transfer their competencies and skills anywhere in the world they choose to live, study, and work.