Artificial intelligence (AI) is impacting education in many different ways. From virtual assistants for personalized education, to student or teacher tracking systems, the potential benefits of AI for education often come with a discussion of its impact on privacy and well-being. At the same time, the social transformation brought about by AI requires reform of traditional education systems. This article discusses what a responsible, trustworthy vision for AI is and how this relates to and affects education.

Open systems are characterized by heterogeneous participants which can enter or leave the system at will. Typical examples are e-commerce applications or information agent systems. Agents (e.g. personal assistants for buying things on the Internet) will only temporarily take up roles (e.g. a buyer in on-line auctions). This creates the need to define precisely what it means that an agent "takes up" a role and "enacts" it. In this paper we present ongoing research on the determination of the conditions under which an agent can enact a role and what it means for the agent to enact a role. We define possible relations between roles and agents and discuss architectural and functional changes that an agent must undergo when it enters an open agent system.

Despite all the research done in the last years on the development of methodologies for designing MAS, there is no methodology suitable for the specification and design of MAS in complex domains where both the agent view and the organizational view can be modeled. Current multiagent approaches either take a centralist, static approach to organizational design or take an emergent view in which agent interactions are not pre-determined, thus making it impossible to make any predictions on the behavior of the whole systems. Most of them also lack a model of the norms in the environment that should rule the (emergent) behavior of the agent society as a whole and/or the actions of individuals. In this paper, we propose a framework for modeling agent organizations, Organizational Model for Normative Institutions (OMNI), that allows the balance of global organizational requirements with the autonomy of individual agents. It specifies global goals of the system independently from those of the specific agents that populate the system. Both the norms that regulate interaction between agents, as well as the contextual meaning of those interactions are important aspects when specifying the organizational structure.

In this paper we introduce and motivate a conceptualization framework for agent-based social simulation, MAIA: Modelling Agent systems based on Institutional Analysis. The MAIA framework is based on Ostrom's Institutional Analysis and Development framework, and provides an extensive set of modelling concepts that is rich enough to capture a large range of complex social phenomena. Developing advanced agent-based models requires substantial experience and knowledge of software development knowledge and skills. MAIA has been developed to help modellers who are unfamiliar with software development to conceptualize and implement agent-based models. It provides the foundation for a conceptualization procedure that guides modellers to adequately capture, analyse, and understand the domain of application, and helps them report explicitly on the motivations behind modelling choices. A web-based application supports conceptualization with MAIA, and outputs an XML file which is used to generate Java code for an executable simulation.