Adaptive learning technology such as intelligent tutoring systems enable the personalisation of students’ learning using similar approaches: they detect the knowledge (or knowledge gaps) of students; they diagnose the next appropriate steps for students’ learning; they act by providing new exercises, new curriculum units, some form of instruction, or just notifying the teacher. This approach is now being expanded beyond mere knowledge acquisition and factoring in behavioural dimensions such as learning self-regulation or style (Chapter 3).
As keeping students engaged and motivated is key to learning effectiveness, a new domain of technology development focuses on measuring engagement and interventions to keep students engaged, both in digital and physical learning environments. Measuring engagement is difficult but a host of new automated approaches have been developed, from eye trackers to the monitoring and analysis of other facial features. Improving engagement typically takes two routes: proactive approaches try to stimulate engagement with incentives, gamification, etc.; reactive approaches do it in a more sophisticated way by continually monitoring engagement, detecting when engagement is waning, and adapting instruction to address periods of disengagement (Chapter 4).
While smart technologies focusing on personalising learning for individuals are probably the most pervasive, another approach is to consider the classroom or rather what happens in the classroom as the subject of the learning analytics. The objective is to support teachers in orchestrating the learning in their classroom and to propose rich and effective learning scenarios to their students. Some classroom analytics techniques provide teachers with real-time feedback to help manage transitions from one task to the next as their students work individually, in small groups or collectively, for example. They also give feedback to teachers on their classroom behaviour so they can reflect on and learn from their practice (Chapter 5).
Social robots are also being increasingly developed for learning uses. Usually powered by the personalisation systems mentioned above, they support teachers in different ways: as instructors or tutors for individuals or small groups, but also as peer learners allowing students to “teach” them. Telepresence robots also allow teachers or students to teach or study remotely and offer new possibilities for students who are ill and cannot physically attend class. They can also mobilise a remotely located teaching workforce, for example teachers from another country to teach foreign languages (Chapter 7).
Technology also enables students with special needs to participate in education and to make inclusive education a reality. With well-known applications such as speech-to-text, text-to-speech, and auto-captioning, etc., AI allows blind, visually impaired, deaf and hard-of-hearing students to participate in traditional educational settings and practices. Some smart technologies facilitate the diagnosis and remediation of some special needs (e.g. dysgraphia) and support the socio-emotional learning of students with autism so they can more easily participate in mainstream education (Chapter 6).
Those smart technologies usually assume and require a human-in-the-loop: a teacher. The level of automation of actions and decisions should be conceived of as a continuum between actions that are fully automated at one end and, at the other end, actions over which humans have full control. As of today, AI systems remain hybrid and request human intervention at a certain point in the process.
