Michael Burkhard

Due to recent advances in natural language processing (NLP), a new generation of digital learning support systems is emerging, which make it possible to analyse the writing quality of texts offering individual, linguistic feedback to writers through various kinds of automated text evaluation. These intelligent tutoring systems (ITS) have to be integrated into existing teaching practices alongside traditional feedback providers (e.g., tutor, peer students). Therefore, this paper explores how academic writing skills of students could be fostered by providing different types of feedback from a tutor, peer students and an ITS. It proposes a feedback model for academic writing in an AI-enabled learning support environment and illustrates the importance of the different feedback providers in an academic writing use case. Through this, the paper aims to contribute to a better understanding of the changing nature of how students' academic writing skills can be fostered in the age of artificial intelligence.

Due to the advances of artificial intelligence (AI) and natural language processing, new kinds of Internet-based writing tools have emerged. Among other things, these AI-powered writing tools can be used by students for text translation, to improve spelling or for rewriting and summarizing texts. On the one hand, they can provide detailed recommendations for the adaptation of text elements within seconds. On the other hand, they also produce inconsistencies and errors, that students might not be aware of. How to deal with these tools in an educational context is a difficult question. Since writing tools are usually used unsupervised and without further instructions, students may need guidance from the teacher in interacting with those tools, to prevent the risk of misapplication. To better understand this underlying issue, the paper at hand uses survey data of 365 freshmen students to describe and analyze student perceptions of AI-powered writing tools. Regarding AI-powered writing tools, different student types were identified by using the k-means clustering method. The results suggest that students have different attitudes towards AI-powered writing tools. Some students may use them in an unreflective, non-sceptical way, which can lead to (un)voluntary plagiarism. Other students may not use writing tools at all, partly because they are sceptic, but also because they may lack meaningful learning strategies in general (course repeaters). Depending on the different student types, individualized teaching strategies might be helpful to promote or urge caution in the use of these tools.

Educational chatbots promise many benefits for teaching and learning. Although chatbot use cases in this research field are rapidly growing, most studies focus on individual users rather than on collaborative group settings. To address this issue, this paper investigates how chatbot-mediated learning can be designed to foster middle school students in team-based assignments. Using an educational design research approach, quality indicators of educational chatbots were derived from the literature, which served as a guideline for the development of the chatbot Tubo (meaning tutoring bot). Tubo is part of a web-based team learning environment in which students can chat with each other and collaboratively work on their group assignments. As a team member and tutor of each group, Tubo guides the students through the learning journey by different scaffolding elements and helps with content-related questions the students have. As part of a first design cycle, the chatbot application was tested with a school class of a technical vocational school in Switzerland. The received feedback suggests that the approach of team-based learning with chatbots has a lot of potential from the students' and teachers' point of view. However, the role distribution of the individual group members may have to be further specified to address the different needs of autonomous as well as more control-oriented students.

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’.

Due to advances in machine learning (ML) and artificial intelligence (AI), computer systems are becoming increasingly intelligent and capable of taking on new tasks (e.g., automatic translation of texts). In education, such AI-powered smart machines (e.g., chatbots, social robots) have the potential to support teachers in the classroom in order to improve the quality of teaching. However, from a teacher’s point of view, it may be unclear which subtasks could be best outsourced to the smart machine. Considering human augmentation, this paper presents a theoretical basis for the use of smart machines in education. It highlights the relative strengths of teachers and smart machines in the classroom and proposes a staged process for assigning classroom tasks. The derived task allocation process can be characterized by its three main steps of 1) break-down of task sequence and rethinking the existing task structure, 2) invariable task assignment (normative and technical considerations), and 3) variable task assignment (efficiency considerations). Based on the comparative strengths of both parties, the derived process ensures that subtasks are assigned as efficiently as possible (variable task assignment), while always granting priority to subtasks of normative importance (invariable task assignment). In this way, the derived task allocation process can serve as a guideline for the design and the implementation of smart machine projects in education.

Social robots are increasingly being used in education. They can take over various roles including teaching assistant, tutor, and novice. This chapter aims to provide a conceptual overview of the phenomenon. A classification of social robots is outlined; the criteria are: visual appearance, social capabilities, and autonomy and intelligence. The majority of robots used in education are humanoid; Nao from SoftBank Robotics is a quasi-standard type. An important social capability is empathy; a model illustrating how a robot can show empathy is discussed. A taxonomy is presented in order to capture the various degrees of robot autonomy. To achieve autonomy, artificial intelligence is necessary. This chapter advocates for a symbiotic design approach where tasks are collaboratively carried out by the teacher and the social robot, utilizing the complementary strength of both parties. This may be in line with the concept of hybrid intelligence. The ethical aspects of social robot use are explored, including privacy, control, responsibility, and the role of teachers. Moreover, the acceptance of social robots is discussed. Overall, attitudes towards social robots seem to be positive; however, there are also contrary findings. Finally, results are presented from a technology acceptance study with a sample of N = 462 university students from the social sciences. The chapter closes with suggestions for further research.

Covid-19 is increasingly forcing educational institutions to explore new avenues and weight the pros and cons between on-site instruction, online instruction, and mixed formats. Understanding teaching format preferences of lecturers may be helpful for creating meaningful solutions with educational technologies. The paper at hand documents and reflects on the organization and implementation of a large-scale first-semester course in Switzerland in the fall term 2020, where seminar lecturers were free to decide on their used course format (on-site, online, mixed). The format preferences of all 39 seminar lecturers were captured and evaluated. Our results indicate that seminar lecturers predominantly opted for mixed or online seminars; often they like to conduct the very first lesson on site for the purpose of becoming familiar with the students.

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.