This article investigates the potential of using Artificial Intelligence (AI) to assess students’ critical thinking skills in higher education. With the growing adoption of AI technologies in educational assessment, there are prospects for streamlining evaluation processes. However, integrating AI in critical thinking assessment remains underexplored. To address this gap, we compare the grading of an educator with that generated by ChatGPT on a critical thinking test for university students. We employ a mixed-methods approach: (a) a quantitative comparison of scores and (b) a thematic analysis to explore the rationale behind the scores. The findings suggest that while AI offers broader contextual feedback, human evaluators provide precision and adherence to grading rubrics, and that universities should consider a hybrid, human and AI evaluation approach. This study contributes to the discourse on how to integrate AI into assessment practices in higher education while addressing issues of transparency and interpretability.

Technology's growing impact is spreading across all aspects of our society, including the realm of K12 education. Past research has mainly focused on STEM subjects, often overlooking the aspects of technical understanding, artifact functionality, design, and problem-solving. As a result, there is a lack of consistent standards in the definition, aims, and implementation of technology education, which affects teachers' competence and confidence in teaching technology. Recognizing this research gap, this study examines first the relationship between teachers' confidence in working with technology and its direct influence on their perceived self-concept of technological skills and, consequently, the overall impact on their self-efficacy in the technological field. We report on quantitative and qualitative data from surveys and interviews from N = 16 German teachers in public primary schools. The results indicate that teachers with low levels of anxiety in working with technology tended to think of themselves as more competent in that area. The findings underpin how confidence in using technology plays a pivotal role in shaping teachers' perceptions of their technological skills, ultimately impacting their confidence in effectively teaching technology-related topics. By highlighting these connections, our study aims to contribute to the research regarding the development of teacher training programs and improve the quality of STEM education.

The demand for competence-orientation in teaching and learning at universities leads to a variety of implications and challenges for technical teaching and learning. To address these demands the premises of 1) the connection among knowledge and its application, 2) student-centering as -well as 3) the space for feedback and interaction are fundamental in the conception of competence-oriented, student-centered learning environments. In the described approach, this is achieved through the elements of topic areas, project tasks, process- and result-related feedback and reflection or interaction formats in the extended context of digital possibilities. Particular potentials of the approach can be expected in bridging the organizational divisions and boundaries of traditional engineering formats such as lectures, exercises and tutorials and bringing together their strengths in coherent learning environments. The conceptual procedure is based on iterative conception-implementation-optimization cycles, in which the experiences of the trainers and feedbacks of the learners are included.

Technical and interpersonal competences are essential characteristics that engineers need to obtain nowadays. Prior studies suggest that empathy and care are important skills in engineering within an increasingly global and innovative work environment. However, there is still limited literature about engineers‘ perceptions of empathy and care particularly when developing curricular approaches to strengthen engineers‘ empathic competences. Thereby, the current study aimed first to validate the factor structure of the US-American Empathy and Care Questionnaire (ECQ), by Hess, Strobel, Pan, and Wachter Morris (2017), regarding German engineers‘ perceptions of empathy and care at their workplaces. Second, the study sought to investigate the differences of perceived empathy of German engineers concerning engineers‘ years of work experience. Results suggest the five-factor model fit was acceptable and that German engineers with more professional experience perceived empathy and care as a more important aspect of their work in comparison to those with less professional experience.