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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.

Social robots have the potential to play a vital role in education. Student technology acceptance may be considered as a driver for their successful integration in educational processes. Based on the ICAP (Interactive, Constructive, Active, Passive) framework and research on social robot roles, we have developed eight vignettes describing scenarios for the use of social robots in higher education, e.g., teaching assistant. Students in an introductory university course completed the vignettes; they answered questions based on the unified theory of acceptance and use of technology (UTAUT), as well as on their ethical approval. Drawing from a sample of N = 361 students, we carried out confirmatory factor analysis. In line with the conceptual basis, eight scenarios can be described. Overall, students do not accept social robots as presenters; however, the assessment of other roles, such as teaching assistant, is positive. By means of a latent profile analysis, we were able to identify four profiles that may be meaningful from a conceptual point of view. One profile is particularly noteworthy. It comprises students who accept social robots not as a means of instruction, e.g., as a tutor, but as a tool, e.g., to promote computational thinking.

Far-reaching technological changes are shaping our society and the ways in which we work. A key 21st -century skill for taking advantage of those changes may be computational thinking (CT). CT aims at enabling humans to carry out more effective problem solving by utilizing concepts of computing and computer technology. For a successful integration of CT into curricula, however, it is important to take assessment into account. We review two instruments that capture CT: the Computational Thinking Test (CTt), a performance test, and the Computational Thinking Scales (CTS), that relies on self-assessment. We have adapted both instruments from English to German. Using a sample of 202 upper-secondary students from Switzerland, we provide further evidence on the validity of both instruments. To this end, we apply item response theory and confirmatory factor analysis. Furthermore, we evaluate the relationship between CTt and CTS. Both instruments show good properties and may be suitable for assessing CT in German-speaking countries at the secondary level. [For the complete proceedings, see ED608557.]

Digital transformation shapes the educational system in many ways. It has also far-reaching implications for teachers as their job description may fundamentally change in the future. In this light, it is important 1) to identify necessary digital competences of teachers and 2) to find ways to foster those competences in an efficient way. By means of a literature review and expert interviews, we developed a framework of teachers' digital competences. In line with Baumert and Kunter (2006) as well as Koehler and Mishra (2009), it comprises content knowledge, pedagogical content knowledge, and pedagogical knowledge. However, these facets have extended meaning in the context of digital transformation. Moreover, our framework considers the official EU competence framework (Carretero et al., 2017) and hence covers instrumental skills and knowledge in handling digital media. We successfully validated our framework by means of structural equation modelling with a sample of 215 Swiss teachers. Utilising an Importance Performance Map Analysis, we identified competence facets that show the highest effects on the (self-reported) use of digital media and content. For efficiently fostering those facets, we will establish online professional learning communities consisting of a communication platform, webinar series, and blended learning courses. [For the complete proceedings, see ED600498.]

The digital transformation challenges teachers to constantly keep up to date with new technologies, often leading to a lack of time for further training and uncertainty in using these technologies. To address these challenges, this conceptual paper introduces the Teacher Copilot, a training and assistant system based on Large Language Models (LLM) and showcases its integration into the curriculum. The aim is to explore the use of generative AI in the teaching context, thereby supporting teachers in their continuous training, teaching, and work processes through AI-based educational technologies. We use the TPACK framework as a guiding structure to foster the types of knowledge necessary for effectively integrating generative AI tools into teaching. The prototype web application was integrated in a course design, serving as a sandbox for future and practicing teachers to experiment with AI-based learning scenarios and to design their own learning scenarios. The evaluation of the course design highlights the novel role of teachers as designers in an AI co-creation process and suggests a potential impact of generative AI on TPACK knowledge dimensions that extends beyond the mere integration of a new tool or technology. The challenges of uncertainty, interdisciplinarity and information overload can be addressed by empowering teachers to integrate AI through co-creation course designs and teacher support systems. [ABSTRACT FROM AUTHOR]

Academic writing has undergone significant evolution due to advancements in AI. Students are leveraging AI in diverse ways for their studies. This study introduces a course design (SOCRAT) to teach students genre-based academic writing through AI. Genre-based academic writing is an educational strategy instructing students in the writing techniques and norms pertinent to their specific academic disciplines. AI is utilised as a personal training system and research assistant in this proposed course design. Students require cognitive and metacognitive knowledge to effectively work with AI tools. The SOCRAT design is based on the concept of mastery learning to ensure that students build their competencies. An initial evaluation of the prompts developed for this design indicates that LLM can particularly assist students in analysing their written text and providing suggestions for enhancement. This can help students develop their genre-based writing skills. The models are not yet convincing for other types of tasks where the LLMs are required to give exact answers. [ABSTRACT FROM AUTHOR]