This paper explores the association of anthropomorphism and cognitive load with respect to the influence of negative attitudes towards robots. The study consists in a cooperative pick-and-place task, where participants are required to repeatedly and alternatingly put a Lego brick onto one of two trays to be picked up and returned by a robot arm. The task is varied by whether or not participants had to remember an 8-digit number inducing extraneous cognitive load (within-subjects factor). Results show significant correlations of some dimensions of anthropomorphism and perceived cognitive load. However, dividing participants in groups with different negative attitudes towards robots, a significant difference of this association is found. This finding puts prior results on the dependency of anthropomorphism of robots and cognitive load into perspective and more research on the underlying cognitive processes is suggested.

Digital communication has played an increasingly important role in schools around the world, especially since the COVID-19 pandemic. For professional communica tion and collaboration in particular, digital tools have provided teachers with the op portunity to collaborate location-independently and to easily exchange information and teaching materials. Hence, this study aimed to explore how teachers communi cate and collaborate digitally by examining differences in the use of instant messag ing and videoconferencing and the attitudes of teachers towards these technologies. Therefore, an online survey was conducted with primary and secondary school teachers from Germany (N = 250, 72.0% female). The analysis showed that mes sengers were used significantly more than videoconferences and that they differed in their usefulness for occasions of communication and collaboration. Structural equation modeling indicated that the self-assessed digital communication compe tence of teachers is both a significant predictor for the behavioral intention to use messengers as well as videoconferences, although the behavioral intention to use messengers in the future was significantly higher than for videoconferences. While a higher threshold to use videoconferences might be a reason for the differences that were identified in this study, further research into the communication and col laboration among teachers is still needed to understand the reasons for the differ ences in use.

KI-Sprachmodelle wie ChatGPT gewinnen an Bedeutung. Zwar liefern sie noch weniger Traffic als Suchmaschinen, doch ihr Einfluss auf die frühen Phasen der Customer Journey steigt. Auch die Abdeckung transaktionaler Journey-Elemente ist nur eine Frage der Zeit.

This study explores a resource-based perspective on subjective wellbeing in educational contexts by introducing the concept of happiness capital. Building on the educational and learning capital approach, we adapted existing measures to assess how personal and environmental resources contribute to students’ wellbeing. In a sample of 1,528 Egyptian secondary school students, perceived happiness capital significantly predicted life satisfaction, positive and negative emotions across all age groups. These findings suggest that the same resource structures supporting learning can foster emotional flourishing. We briefly discuss implications for resource-based models of wellbeing in education and propose directions for future research.

 

Emotion AI is transforming how emotional dynamics are integrated into consumer research and product design. By detecting and interpreting emotional cues from facial expressions, voice, and behaviour, it enables emotionally intelligent systems that move beyond conventional user interaction models. This chapter examines Emotion AI as both a methodological tool and a design principle, offering deeper insights into user sentiment and supporting the creation of hedonic experiences – what we term digital empathy. Grounded in established emotion theories and recent technological advances, the chapter explores cross-sector applications and reflects on the ethical implications surrounding the use of emotional data.

·       Hyperpersonalisierung und Extended Reality (XR) verändern die Customer Journey

·       KI-gestützte Personalisierung gestaltet Markenkommunikation und Kundenerlebnisse emotionaler und interaktiver

·       Extended Reality (XR) eröffnen neue Dimensionen immersiver Markenerlebnisse

·       Immersive Consumer Experience bindet Konsumenten stärker an Marken und erleichtern die Kaufentscheidung

Der Beitrag stellt den Entwicklungsprozess des im Projekt LeadCom entstandenen Selbstlernkurses Videokonferenznutzung zur kollegialen Kommunikation und Kooperation dar und zeigt auf, warum diese Fortbildung zum Thema Videokonferenzen in außerunterrichtlichen Situationen für eine zeitgemäße Schulentwicklung relevant ist. Einerseits wurde der Kurs mit dem Ziel konstruiert, durch eine kompetente Nutzung von virtuellen Meetings im Schulkontext, eine moderne und effektive Zusammenarbeit im Lehrerkollegium zu fördern und so den sich verändernden Ansprüchen einer zunehmend digitalisierten Welt gerecht zu werden. Andererseits soll die Nutzung des Selbstlernkurses auch dazu beitragen, dass Lehrkräfte und Schulleitungen sich digital kompetent und selbstwirksam im Umgang mit digitalen Kommunikationsmedien erleben. Als Transferziel sollten die Einstellungen und Erwartungen von Lehrkräften gegenüber Informations- und Kommunikationstechnologien (ICT) und die Nutzung von ICT für schulinterne Kooperation die Nutzung von ICT im Unterricht sowie die digitale Kompetenzentwicklung von Schülerinnen und Schülern positiv beeinflussen (u.a. Drossel et al., 2016; Fraillon et al., 2020). Die Entwicklung des Selbstlernkurses fußte dabei unter anderem auf dem weiterentwickelten Technologieakzeptanzmodell von Venkatesh et al. (2003, 2012), der Unified Theory of Acceptance and Use of Technology (UTAUT). In einer eng mit dem Entwicklungsprozess der Fortbildungseinheit durchgeführten quantitativen Studie wurden anhand des UTAUT-Modells Einstellungen und Erwartungen von Lehrkräften in Bezug auf die Nutzung von Videokonferenzen in außerunterrichtlichen Kommunikations- und Kooperationssituationen untersucht. Der Beitrag wird aufzeigen, wie sowohl die theoretischen Überlegungen aus dem UTAUT-Modell als auch in Teilen die Ergebnisse der Studie in die Konstruktion des Selbstlernkurses eingeflossen sind.

Systemic and learning resource-oriented talent development emphasizes interplays of resources such as the law of the minimum, which states that the least resource limits talent development. In the present study, 1693 Sudanese students completed the Questionnaire for Educational and Learning Capital. First, the factorial validity was confirmed. Subsequently, four hypotheses regarding the law of the minimum were tested and partially confirmed: (1) The minimum of exogenous learning resources (in the environment of the students) explained substantial proportions of the variance of endogenous learning resources (within the students). (2) The minimum exogenous resource explains a similar amount of variance within endogenous learning resources than a mean of several minimum scores. (3) Additional compensation possibilities did explain significantly more variance of the endogenous resources than just the law of the minimum. (4) In line with the law of the minimum paradox, a cluster analysis identified a group of students with higher correlated learning resources (less well adapted system) than a second group of students. The results are discussed to encourage further research into systemic interplays of learning resources and ideas toward a systemic gifted and talented education.

Generative KI-Tools wie ChatGPT eröffnen neue Möglichkeiten für das Lernen – von schnellen Erklärungen bis hin zu personalisierter Unterstützung. Für erfolgreiche Lernprozesse sollten Lernende aber nicht alle Denkprozesse auslagern (cognitive offloading), den eigenen Lernfortschritt im Blick behalten (metacognitive laziness) und die KI als aktiven Lernpartner nutzen – nicht nur als Suchmaschine (co-creation). Im Vortrag wird vorgestellt, wie diese KI-Interaktionen im Projekt SekoKI beforscht werden.

Learning in the digital world requires not only technological skills for using digital tools but also ethical skills to critically reflect on (in)adequate digital media use and potential negative consequences. These skills are particularly crucial in professions dealing with public welfare and societal issues. Inter alia, those are teachers who educate the youth, legal professionals who judge (il)legal behavior regarding media, or computer scientists who bear responsibility when developing algorithms. Accordingly, higher education students studying teacher education, law studies, or computer science studies should develop ethical skills for the digital world. This study examined how higher education students perceive problematic media behaviors and which digital competences they regard relevant for ethical issues. In addition, the study investigated whether students of teacher education, law studies, and computer science studies differ in their perceptions. To this end, an online survey with N = 461 participating students was conducted. Study results indicated that higher education students perceived problematic media behaviors as such with posting inappropriate content identified as the most problematic. Furthermore, students considered several digital competences as relevant for ethical issues with protecting and acting safely as most relevant. In-depth analyses uncovered subject-specific differences with computer science students being most ethically savvy, albeit differences were only of small effect size. The study provides valuable insights into the intersection of digital competences, ethical considerations, and academic disciplines. In the future, longitudinal and training studies will help to understand how differences emerge and whether students of different study subjects benefit from digital ethics training.

Does the AI agree? Inter-rater agreement in learning diary evaluation

Theory

Learning diaries as formative assessments are promising to support the learning process and stimulate reflection. In an open learning diary, learners apply learning strategies to reflect on their learning and deepen their knowledge. To guide learners, learning diaries can be structured according to different learning strategies. However, grading and feedback on learning diaries is effortful for teachers. Artificial intelligence (AI) may assist teachers in the evaluation process. A prerequisite is that AI and teachers show high inter-rater agreement.

Research Questions

The current study aimed to analyze the agreement between teachers and ChatGPT-4o by examining four separately assessed learning strategy categories of a learning diary in adult education (organization, in-depth elaboration, transfer-supporting elaboration, and metacognition). The two research questions were:

• Q1: How accurate is the overall agreement between teachers and ChatGPT-4o, and are there differences across different learning strategy categories?

• Q2: Does the inter-rater agreement differ between teachers across the four learning strategy categories?

Method

Seven different adult education teachers and ChatGPT-4o evaluated a total of 540 learning diary entries. Each teacher assessed approximately 65 entries. Teachers were trained in criteria-based evaluation per learning strategy category. An engineered prompt supported the ChatGPT-4o model.

Teacher ratings served as the reference for the inter-rater agreement. Absolute accuracy and under-/overestimation (bias) were calculated for each learning strategy category as accuracy measures. Furthermore, overall accuracy values were calculated across the four categories for absolute accuracy and bias.

A doubly multivariate repeated measures ANOVA was conducted with the four learning strategy categories as repeated measures and absolute accuracy and bias as measures to test for accuracy differences between the learning strategy categories (Q1). Additionally, teacher was used as a between-subjects factor. Thus, the interaction of the learning strategy category and rating teacher regarding accuracy could be tested statistically (Q2).