This paper presents an extended version of the SPADE platform, which aims to empower intelligent agent systems with normative reasoning and value alignment capabilities. Normative reasoning involves evaluating social norms and their impact on decision-making, while value alignment ensures agents' actions are in line with desired principles and ethical guidelines. The extended platform equips agents with normative awareness and reasoning capabilities based on deontic logic, allowing them to assess the appropriateness of their actions and make informed decisions. By integrating normative reasoning and value alignment, the platform enhances agents' social intelligence and promotes responsible and ethical behaviors in complex environments.

Value-alignment in normative multi-agent systems is used to promote a certain value and to ensure the consistent behavior of agents in autonomous intelligent systems with human values. However, the current literature is limited to incorporation of effective norms for single value alignment with no consideration of agents' heterogeneity and the requirement of simultaneous promotion and alignment of multiple values. This research proposes a multi-value promotion model that uses multi-objective evolutionary algorithms to produce the optimum parametric set of norms that is aligned with multiple simultaneous values of heterogeneous agents and the system. To understand various aspects of this complex problem, several evolutionary algorithms were used to find a set of optimised norm parameters considering two toy tax scenarios with two and five values are considered. The results are analysed from different perspectives to show the impact of a selected evolutionary algorithm on the solution, and the importance of understanding the relation between values when prioritising them.

Explainability is a fascinating topic. It covers a research field where a wide variety of experts come together: mathematicians, engineers, psychologists, philosophers and regulators, which makes it one of the most interesting. I have been involved in quite some AI projects where explainability -- or XAI -- turned out to be crucial. So, I decided to gather and share my experiences, and the experiences of my colleagues at Deeploy. AI is one of the biggest innovations of our time. It can change the way we live, work, care, teach and interact with each other.

Prologue: This is not an introduction to AI or AI in education. You can find these here and here, respectively. Rather, this is a constellation of positions on AI from an educator's perspective. I pull together disparate aspects of current debates about AI and education in order to articulate some directions for the field. Lately I've been sitting in rooms with a lot of thoughtful people from industry, different disciplines in academia, and policy backgrounds to think through artificial intelligence (AI) and education.

Artificial Intelligence technology development, application, and production are growing rapidly. As we being to understand the scope of the change that lies ahead, two simple questions come to mind. First, is it even possible to ensure that the technology is developed solely for the benefit of the humankind and not to cause harm? Second, if such an ideal is achieved, who controls and monitors it? The first assumption we have to make – and frankly it is not a big leap – is that artificial intelligence technology, like other technologies, can be used to do both good and bad.