Smart learning environments (SLEs) are defined in this paper as physical environments that are enriched with digital, context-aware and adaptive devices, to promote better and faster learning. In order to identify the requirements for ‘better and faster learning’, the idea of Human Learning Interfaces (HLI) is presented, i.e. the set of learning related interaction mechanisms that humans expose to the outside world that can be used to control, stimulate and facilitate their learning processes. It is assumed that humans have and use these HLIs for all types of learning, and that others, such as parents, teachers, friends, and digital devices can interact with the interface to help a person to learn something. Three basic HLIs are identified that represent three distinct types of learning: learning to deal with new situations (identification), learning to behave in a social group (socialization) and learning by creating something (creation). These three HLIs involve a change in cognitive representations and behavior. Performance can be increased using the practice HLI, and meta-cognitive development is supported by the reflection HLI. This analysis of HLIs is used to identify the conditions for the development of effective smart learning environments and a research agenda for SLEs.

The provision of lifelong learning facilities is considered to be a major new direction for higher and distance teaching educational institutes catering for the demands of industry and society. ICT networks will in future support seamless, ubiquitous access to lifelong learning facilities at home, at work, in schools and universities. This implies the development of new ways of organizing learning delivery that goes beyond course and programme-centric models. It envisions a learner-centred, learner-controlled model of distributed lifelong learning. We present a conceptual model for the support of lifelong learning which is based on notions from self-organization theory, learning communities, agent technologies and learning technology specifications such as IMS Learning Design. An exploratory implementation has been developed and used in practice. We reflect on the findings and future directions.

IMS Learning Design (IMS-LD) is a specification to create units of learning (UoLs), which express a certain pedagogical model or strategy (e.g., adaptive learning with games). However, the authoring process of a UoL remains difficult because of the lack of high-level authoring tools for IMS-LD, even more so when the focus is on specific topics, such as educational eGames. However, external tools that are not specifically IMS-LD oriented can be used. In this case, the main challenge is the integration between these external resources developed with other technologies and the personalized learning experience of an IMS-LD UoL. In this article, the authors use the <e-Adventure> project to develop conversational games that are integrated with IMS-LD UoLs to improve personalized learning. The main contribution of this setting is the integration of both parts, the IMS-LD specification and <e-Adventure>, and the communication that enables a mutual influence of the adaptive learning experience.