Explainable artificial intelligence for education and training

Researchers and software users benefit from the rapid growth of artificial intelligence (AI) to an unprecedented extent in various domains where automated intelligent action is required. However, as they continue to engage with AI, they also begin to understand the limitations and risks associated with ceding control and decision-making to not always transparent artificial computer agents. Understanding of “what is happening in the black box” becomes feasible with explainable AI (XAI) methods designed to mitigate these risks and introduce trust into human-AI interactions. Our study reviews the essential capabilities, limitations, and desiderata of XAI tools developed over recent years and reviews the history of XAI and AI in education (AIED). We present different approaches to AI and XAI from the viewpoint of researchers focused on AIED in comparison with researchers focused on AI and machine learning (ML). We conclude that both groups of interest desire increased efforts to obtain improved XAI tools; however, these groups formulate different target user groups and expectations regarding XAI features and provide different examples of possible achievements. We summarize these viewpoints and provide guidelines for scientists looking to incorporate XAI into their own work.

Reconstructing and Reorganizing Experience: Weaving a Living Philosophy

Background/Context This study contributes to ongoing work in professional learning communities, self-study, and reflection. It offers a structure and a process rooted in a philosophy of practice grounded in various thinkers like Dewey, Rogers, Curran, Freire, Gattegno, Greene, and Carini. Purpose/Objective The purpose of this study is to explore the process of one teacher educator inquiry group that has lasted nearly 30 years, and which has sought to enact Dewey's notion of education, “that reconstruction or reorganization of experience that adds meaning to experience and increases ability to direct the course of subsequent experience.” Research Design Our research design mirrors the reflective inquiry process of our group, that is, how we researched is simultaneously what we researched. The process itself follows a structure for reflection outlined by Dewey in Rodgers. It begins with recollected experiences, descriptions of those experiences, and is followed by a “harvesting of themes,” through analysis and interpretation, to possible modes of “intelligent action.” The process is iterative and continuous. Conclusions Our conclusions go beyond mere descriptions of our process. We come to the realization that, as Greene writes, being “wide-awake” to the particularities of our lives and work; to live intentionally, deliberately, and morally; and to be aware of who and why we are, and are to each other, is essential to a democratic society.

Knowledge-Driven Autonomous Robotic Action Planning for Industry 4.0

Autonomous robots are being increasingly integrated into manufacturing, supply chain, and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex Industry 4.0 tasks, the autonomous robots require robust action plans that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases that may be queried during planning and execution. In this chapter, the authors propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, they model the knowledge of world models, robotic capabilities, and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning. At runtime, integrity constraints on world model observations are used to update knowledge bases. They demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses.

Current Perspectives for Autonomous Sensor Nodes

The current technological trends associated with Industry 4.0 and the Internet of Things (IoT) require an interconnected network of sensor nodes providing distributed information on the environment to enable intelligent action to be taken by control systems. Such sensors need to be wireless, self-powered and energy independent. In this work we provide an overview of possible strategies to realize a positive energy balance in autonomous sensor nodes without the use of batteries. We will first overview different sensors in terms of power consumption. We will then concentrate on energy harvesting and storage, showing state-of-the-art possibilities in both cases.

2. The evolution of emotion

‘The evolution of emotion’ argues that emotions were—and still are—vital for survival. Ever since Plato, many Western thinkers have tended to view emotions as obstacles to intelligent action. We are all familiar with cases in which an excess of emotion prevents people from acting intelligently. The opposite idea—the positive view of emotion—is that emotions are vital for intelligent action. Until recently, this idea had not been popular among philosophers and psychologists, but considerations drawn from evolutionary theory and neuroscience now argue in its favour. The evolution of emotions such as fear, anger, joy, disgust, guilt, love, and revenge are considered along with the relationship between emotions and morality.