AI and Data

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Using data in vocational and education training schools, universities of applied sciences and research universities offers opportunities to organise education smarter and personalise education for learners. The three sectors have similar needs in that area: how can the use of learning data, learning analytics and AI contribute to personalising education? How do we improve automated feedback delivery, improve study advice, curriculum construction and educational research using learning data? And how can the use of data and AI provide insights for effective interventions?     

For each type of education, institutions are looking to work together. There are also more and more issues where an individual institution is not getting anywhere. As a result, there is a growing need for agreements around legal and ethical frameworks, an autonomous and flexible digital infrastructure, control over data and shared, proven approaches. By sharing knowledge and experiences, there is no need for everyone to reinvent the wheel. In this way, institutions are better able to monitor learners’ wellbeing, progress and risk of dropping out (student success) and respond with targeted interventions. Learners gain insight into their own learning process and tools to guide it. Lecturers are better able to increase the effectiveness of their teaching. Researchers use data as a source for evidence-informed educational innovation. Lifelong learning and the transformation hub Agile and efficiently organised education are supported by the use of data.