The Future of Education

AI-based tools are increasingly transforming higher education, leading to significant advances in educational methodology. Educators need to classify and select AI-based pedagogical practices. Commonly, AI-related use-cases in higher education are usually characterized on technological details of the software tool as interaction type or the medium generated. We propose a structured taxonomy to classify various use-cases in higher education based on the teacher’s motivation. We do not focus on what properties of a particular AI tool, but what its advantages in teaching are. The proposed taxonomy attempts to provide educators to weigh and select AI solutions based on an explicit understanding of their educational benefits and limitations. By evaluating the three dimensions: repetition, data access, and semantic discrimination, teachers can better understand why a specific teaching context benefits from AI. After a brief overview on recent literature, we introduce a novel classification scheme that helps educators understand the rationale for selecting particular use-cases. To illustrate the applicability, we provide examples of selected educational use-cases classified with the proposed scheme.