Ludwigsburg University of Education

Abstract This exploratory study examined the interplay between self‐regulation, uncertainty in mentalizing, and pedagogical competence in novice special education teachers. Drawing on conceptual frameworks highlighting the importance of adaptive self‐regulation for effective teaching, the study also investigated whether impaired mentalizing independently affects pedagogical performance. A sample of 707 novice teachers in special needs education completed self‐report measures assessing self‐regulation, uncertainty in mentalizing, and pedagogical competence. According to their self-regulatory capacities, participants were assigned to four groups. Multivariate analyses of covariance and univariate tests revealed that special education teachers with adaptive self‐regulatory patterns demonstrated higher pedagogical competence in teaching, educating, and assessing. In contrast, uncertainty in mentalizing was associated with lower overall pedagogical competence, with the most substantial negative effects observed in the domains of assessing and innovating. The findings suggest that both adaptive self‐regulation and effective mentalizing might reflect important determinants of effective pedagogical practice. Practical implications are discussed.

Generative Artificial Intelligence (GenAI) is reshaping education by introducing tools that enhance teaching methodologies, personalize learning, and streamline administrative tasks. However, adoption of these tools remains uneven, raising concerns about disparities in AI literacy and competency across geographic regions and educational contexts. Through the lens of Innovation Diffusion Theory, we investigated the adoption of GenAI tools among second language (L2) teachers in the United States, Colombia, Germany, and Macau. Using survey data, we assessed four areas: accessibility of GenAI tools, teacher knowledge of potential applications, integration in teaching practices, and the nature of professional development provided. Our results indicated substantial intra- and inter-context variance, with U.S. and Colombian educators reporting higher familiarity and usage compared to those from Germany and Macau. Additionally, university and high school teachers were more likely to access professional development and leverage GenAI for tasks like assessment and differentiation than elementary or middle school educators, regardless of geographic setting. These disparities align with broader trends in AI adoption, reflecting heterogeneity in cultural attitudes, systemic barriers, and institutional support. These findings contribute to Innovation Diffusion Theory by illustrating how accessibility, knowledge, and institutional support interact across diverse contexts. They offer practical guidance for policymakers and educational leaders to develop targeted interventions that promote equitable GenAI integration in language education worldwide. • We find an AI divide among L2 teachers associated with training and support gaps. • Disparities highlight emerging geographic divides in AI literacy and competency. • Government policies and institutional priorities influence GenAI adoption. • Targeted strategies are critical to mitigate gaps in adoption for L2 education.

IntroductionThe integration of advanced digital applications within the educational sector remains a significant challenge. It has been determined that extracurricular educational settings possess a considerable capacity to enhance and differentiate digital instruction. However, educators frequently require assistance to transition from the use of existing digital resources to the creation of their own, personalized, customized learning materials. Professional learning communities (PLCs) have been identified as a promising framework in which teachers can collaborate to develop innovative and practical concepts. While the potential of PLCs is widely recognized, research on this topic is fragmented. A multitude of definitions and operationalizations have been proposed regarding the structure and functioning of PLCs. This complicates a systematic investigation of their effectiveness and makes it difficult to distinguish empirically based findings from purely anecdotal reports. Consequently, a definitive evidence base for the design of effective PLCs remains elusive.MethodsThe present study posits that the afore mentioned gap is addressed by means of a scoping review. Utilizing the PRISMA-ScR framework, a systematic analysis of German and English-language literature is conducted with the objective of mapping and synthesizing research on the composition, collaboration, and effectiveness of PLCs in the STEM field or in relation to digitalization.ResultsThe results of the study provide a structured overview of the key characteristics, structure, and success factors of PLCs. The present article elucidates the findings that emerged from a comparative and harmonising analysis of diverging PLCs.DiscussionThe ensuing discussion pertains to the configuration, efficacious design, and execution of professional learning communities, in conjunction with their operational methodologies and investigative endeavours. This synthesis provides a foundation for the conceptualisation of future cooperative initiatives aimed at the development of STEM- or digital educational resources.

Continuous professional development of science teachers is closely related to their ability to reflect on their science lessons. For teachers to effectively reflect on their lesson, they need opportunities to make meaningful teaching-related experiences and receive guiding feedback for their reflections during initial teacher education. A sensible means to provide reflection opportunities for teachers is through written reflection assignments. However, assessing written reflections is conceptually challenging and resource-intensive. Recent developments suggest that artificial intelligence (AI) might provide novel opportunities for science teacher education and assessing written reflections. Consequently, we investigate ways in which AI in the form of a large language model can be utilized in a specific science teacher training program. The potential of AI for the use in higher education have been highlighted in recent years. However, it is noted that large language models oftentimes are too generic to be useful in specific science teacher education contexts. To address this issue, we used a pre-trained large language model and fine-tuned it to provide individualized feedback for pre-service physics teachers’ written reflection and investigated to which degree contextual factors impact the classification accuracy. Our results show that the pre-trained language model allowed a sufficient classification and that the classification accuracy can be improved by context specification. Our study highlights opportunities and challenges when utilizing AI, in particular large language models, for reflective writing analytics in science education.

Abstract Background Heart failure (HF) is one of the leading public health issues worldwide. Current guidelines recommend HF treatment using different drug classes depending on ejection fraction (EF) and underlying cause of HF. Sodium-glucose co-transporter 2 inhibitors (SGLT2i) are a drug class with a class IA recommendation in the current ESC-guidelines for HF treatment independent of EF. The SGLT2i dapagliflozin is approved for treatment of HF across the entire spectrum of EF in Germany. However, real-world data are limited. Purpose The aim of EVOLUTION-HF DEallEF is to generate data on real-world dapagliflozin use in HF patients independently of EF in Germany under the multinational umbrella EVOLUTION-HF study protocol. Methods EVOLUTION-HF DEallEF is a non-interventional, prospective, longitudinal cohort study. It is planned to enrol a total of 1000 patients with chronic HF in Germany, including 40% patients with preserved (HFpEF; EF ≥ 50%), 20% with mildly reduced (HFmrEF; EF 41-49%), and 40% with reduced EF (HFrEF; EF ≤ 40%). Patients willing to participate, aged ≥18 years, who started dapagliflozin treatment 14 to 90 days prior to enrolment according to the local product label, are eligible for participation. Among reasons for exclusion is previous SGLT2i treatment (including dapagliflozin). Data on medical history, clinical symptoms, previous and concomitant medication and patient-reported outcomes (PROs) are collected. EVOLUTION-HF DEallEF has included its first patient in 04/2024 and is currently open for enrolment. Interim analyses (IA) were planned at predefined timepoints (after 250, 500 enrolled patients and enrolment completion). The three different EF cohorts (HFpEF, HFmrEF, HFrEF) are analysed separately and as a total population. Only descriptive statistical methods are used. Results This 1st IA (data cutoff: 02/12/2024) included 249 patients in the safety set (all patients received at least one dose of dapagliflozin, one patient refused data usage) and 244 patients who fulfilled all eligibility criteria in the full analysis set (FAS). Patients were enrolled in 31 study sites, mainly in office-based cardiologic practices. In the FAS, distribution of patients to HF cohorts was 51% with HFpEF (n=125), 30% with HFmrEF (n=73), and 19% with HFrEF (n=46). Conclusions Patients’ and disease characteristics observed at the time of the first IA are in range with previously published data indicating that a representative cohort of patients newly treated with dapagliflozin for HF are enrolled in EVOLUTION-HF DEallEF. Dysbalance in expected enrolment proportion per EF cohort is mainly caused by exclusion of patients with SGLT2i-pretreatment. The study aims to provide further insights into real-world dapagliflozin treatment for HF in Germany, focussing on clinical and patients’ perspective.

We investigate the applicability of visual foundation models, a recent advancement in artificial intelligence, for archaeological remote sensing. In contrast to earlier approaches, we employ a strictly zero-shot methodology, testing the hypothesis that such models can perform archaeological feature detection without any fine-tuning or other adaptation for the remote sensing domain. Across five experiments using satellite imagery, aerial LiDAR, and drone video data, we assess the models’ ability to detect archaeological features. Our results demonstrate that such foundation models can achieve detection performance comparable to that of human experts and established automated methods. A key advantage lies in the substantial reduction of required human effort and the elimination of the need for training data. To support reproducibility and future experimentation, we provide open-source scripts and datasets and suggest a novel workflow for remote sensing projects. If current trends persist, foundation models may offer a scalable and accessible alternative to conventional archaeological prospection.

Fairness is a fundamental principle in human social interactions that influences subsequent behavioural decisions. As artificial intelligence (AI) becomes more prevalent, human-computer interactions have emerged as a new mode of social interaction. This study investigates the differences in fairness perceptions and their impact on trust decisions in human-human and human-AI contexts using a mixed experimental design of 2 (proposer identity: AI/human) × 2 (offer: fair/unfair) × 2 (trustee identity: AI/human). A total of 128 university students participated in the experimental study employing both the Ultimatum Game and the Trust Game paradigms. The results showed that participants who received fair offers had higher trust investment rates and amounts than those who received unfair offers. When offers were unfair, the AI proposer group elicited greater investment willingness, leading to higher trust investment rates than the human proposer group. Conversely, under fair conditions, participants displayed greater risk aversion towards human trustees, investing at lower rates and amounts than with AI trustees. The findings suggest that fairness perceptions in human-computer interactions have a stronger impact on trust decisions than those in human-human interactions.