The use of Artificial Intelligence (AI) in education

Summary:

There are two different types of AI in wide use today. Recent developments have focused on data-driven machine learning, but in the last decades, most AI applications in education (AIEd) have been based on representational / knowledge-based AI. Data-driven AI uses a programming paradigm that is new to most computing professionals. It requires competences which are different from traditional programming and computational thinking. It opens up new ways to use computing and digital devices. But the development of state-of-the-art AI is now starting to exceed the computational capacity of the largest AI developers.

The recent rapid developments in data-driven AI may not be sustainable. The impact of AI in education will depend on how learning and competence needs change, as AI will be widely used in the society and economy. AIEd should be used to help schools and educational institutions in transforming learning for the future. Many AIEd systems have been developed over the years, but few of these have shown clear scientific impact on learning. Evidence is lacking partly because the contexts of teaching and learning vary across classrooms, schools, educational systems, and countries. Local knowledge and capacity is critical for effective adoption and shaping of AIEd, and new scaling models are needed. Co-design of AIEd with teachers is a possible way to advance new scaling models.

AI has a great potential in compensating learning difficulties and supporting teachers. The Union / the EU needs a “clearing house” that helps teachers and policy-makers make sense of the fast developments in this area.