Intelligent Frameworks for Assessment in AI-Enhanced Learning Environments

Nowadays several critical challenges, opportunities, and trends in learning must be considered in the development and implementation of new learning environments.

Nowadays several critical challenges, opportunities, and trends in learning must be considered in the development and implementation of new learning environments. These include encouraging lifelong learning, valuing both informal and formal learning, addressing the open and social dimensions of learning, and recognising the different contexts where learning takes place. It is also crucial to address what today’s students need. We observe, however, that during the last years, knowledge acquisition and learning have been distributed and continue to occur in a world without boundaries. Students are collaborating more than ever beyond classroom boundaries, which become more and more irrelevant within formal settings. Moreover, the openness of knowledge resources and the social nature of the web through the participation, voting, collaboration, aggregation, and distribution it enables, are leading to a new generation of students, driven by openness, networking, and sharing. Considering the new requirements in terms of learning also raises challenges with regard to the assessment of learning. Student-centred and networked learning require new assessment models that address how to recognise and evaluate self-directed learning achievements.