In academic writing, the competency to argue is important. However, first-year students often have difficulties to construct good arguments. Advances in natural language processing (NLP) have made it possible to better analyze the writing quality of texts. New tools have emerged which can give students individual feedback on their texts and the structure of their arguments. While the use of these argumentation learning support tools can help create better texts, using them in an academic context also carries risks. Learning scenarios are needed that promote argumentation competency using argumentation tools while also making students aware of their limitations. To address this issue, this paper investigates how a learning design with an argumentation learning support tool can be developed to increase the argumentation competency of first-year students. The conjecture-mapping technique was used, to visualize our assumptions and illustrate the developed learning design. As part of a fi rst design cycle, the learning design was tested with 80 students in seven academic writing classes at the University of St.Gallen in Switzerland. Preliminary findings suggest that the learning design might be helpful to improve the argumentation competency as well as the data-literacy of students (in relation to argumentation tools). However, further research is necessary to confirm or reject our hypotheses.

In education, smart machines (e.g., chatbots or social robots) have the potential to support teachers in the classroom in order to improve the quality of teaching. From a teacher's point of view, smart machines also pose a challenge because the presence of smart machines in the classroom questions traditional teacher and student roles. This paper presents a theoretical basis for the use of smart machines in education. It describes the relative strengths of teachers and smart machines and presents them in a framework, which makes a proposal for an augmented task sharing. In light of human augmentation, the framework proposes ways in which teachers can position themselves with regard to smart machines in a complementary and mutually reinforcing way. It also has implications for knowledge that is necessary for teachers to play an active role in the digital transformation.

Smart machines (e.g., chatbots, social robots) are increasingly able to perform cognitive tasks and become more compatible with us. What are the implications of this new situation for the competency requirements in the 21st century? This paper evaluates the underlying paradigm shift with relation to smart machines in education. It discusses the potentials and current limitations of smart machines in education in order to eliminate prejudices and to contribute to a more comprehensive picture of the technological advances. In light of human augmentation, the paper further proposes a possible learning framework that includes the human-smart machine relationship as a normative orientation for new competency requirements.

This paper provides an overview of the current state of research on social robots in higher education and the existing frameworks to categorize and develop social robot applications. Based on the existing work, we present our own framework to develop use cases for social robots in the education sector. Our framework is based on a heuristic and symbiotic design approach that serves as a guideline for developing use cases and views human-robot interaction as two complementary and mutually reinforcing roles. We illustrate our framework by means of a use case that we have conducted in 2019 during the initial lecture of the large-scale course ‘Introduction to academic writing’.