This study investigates the division of labor between students and generative AI in solving tasks addressing critical thinking. Three key research interests guide the study: (1) the extent of ChatGPT usage by students, (2) the division of labour between student and AI as perceived by students, ChatGPT, and an expert, and (3) if these ratings align with each other. An experiment involving 20 undergraduate students who completed 11 tasks using ChatGPT was carried out. Findings demonstrated substantial variation in the extent of ChatGPT usage among students. The division of labour analysis revealed that ChatGPT is used most heavily for reasoning tasks, and that students consistently rated the AI's contribution to their task solutions as slightly lower than both ChatGPT and the expert. Correlations of the ratings indicated strong alignment between students and ChatGPT, but weaker alignment with the expert perspective, particularly in the metacognition domain. These results highlight the need for enhanced integration of AI in educational practices, focusing on fostering students' understanding of AI capabilities while promoting critical engagement. Future research should explore diverse populations, a broader range of task types, and the long-term impact of AI integration to enhance the effective use of generative AI in education.

The emergence of ChatGPT has significantly reshaped the landscape of higher education, sparking concerns about its potential misuse for academic plagiarism (Cotton et al., 2023). This study examines the use of ChatGPT in academic writing among students at the University of Mannheim in Germany and St. Gallen in Switzerland, using a proposed Human-AI collaboration framework with six levels of AI-enabled text generation (Boyd-Graber et al., 2023). The survey of 699 students reveals varied ChatGPT usage across all six levels, with Level 3 (Literature Search) being slightly more utilized. Students expressed mixed opinions on ethical issues, such as the declaration of ChatGPT-generated content in academic work and the extent to which ChatGPT is allowed at their university. The results of the study highlight students' concerns about negative effects on grades, a lack of clarity about university policies on ChatGPT, and fears that hard work will not be rewarded. Despite these issues, most students support open access to ChatGPT. The findings suggest the need for clear ethical guidelines in academia regarding AI use and highlight the potential stigmatization of AI, which could hinder technology acceptance and AI-related skills development

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.

While chatbots or conversational agents are already common in many business areas, e.g. for customer support, their use in the education sector is still in its infancy. Chatbots might take over the role of a teacher, tutor, conversational partner, learning analyst, team member, support assistant, or recommender system. Within these different roles, chatbots can enhance learning and inherently address many requirements and success factors for learning. The scalability and adaptiveness of conversational AI allow an individualised learning support for all learners combined with collaboration opportunities and thus more equality in education. In this context, the paper at hand discusses this pedagogical potential of chatbots in different roles and social settings resulting in a conceptual framework for the understanding and design of chatbot use cases in education. Based on success factors for learning derived from established learning theories and reports, core attributes and goals of chatbot learning are deducted within three pedagogical domains of individual, social and analytic chatbot learning. By combining this pedagogical dimension with a technological and content dimension, the presented conceptual framework provides an overview of possibilities of how chatbots in education can be used and designed.

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’.

The aim of the paper is to address the question how social media can be successfully used for trust and reputation building in the learning function, in particular for eLearning at the workplace in organizations. The article consolidates the findings in a frame of reference with a two folded approach from a management perspective (using social media to organize learning) as well as from a learning design perspective (using social media to design learning environments).