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).

Laser technology presents a compelling alternative to conventional methods for removing coatings from plastic and metallic parts, offering advantages over chemical solvents and media blasting for component reuse and recycling. This study investigates laser paint removal from thermoplastic materials, particularly those with complex 3D geometries. Utilizing a 1064 nm pulsed fiber laser, experiments were conducted to analyze paint ablation from plastic substrates and characterize the resulting surfaces. Experimental results on plastic substrates demonstrate the feasibility of complete paint removal while preserving the integrity of the thermoplastic. The laser's energy density, scanning speed, and spot size are identified as key parameters influencing removal efficiency and substrate integrity. The study presents the necessary steps and process conditions and analyzes the resulting quality of the processed surfaces. The study concludes that laser paint removal facilitates the reuse or high-quality recycling of plastic parts and can be an environmentally friendly, flexible, and highly efficient method for paint removal.

Reverse engineering encompasses an array of technologies and methods which produce results in terms of design, simulation and manufacturing. Options are pushed a little further by a new set of digital tools and scientific methods. Finite element methods (FEM) can validate models and propose structurally optimised geometries or attain prolonged life in service. 3D additive manufacturing processes can be simulated making it possible to pin point problematic areas. With respect to product design and digital manufacturing, reverse engineering uses a number of techniques, tools, methods and means of optimization which ease the process of re-manufacturing or use of an enhanced reinterpreted product. Of course, there are legal and ethical concerns that must be met within legal boundaries. Reverse engineering can be corroborated with other techniques of data interpretation and manipulation in such a way that results are both time-and-cost effective. Easy integration with Industry 5.0 concept is an ongoing process that proposes sustainable optimization of energy consumption, materials processing, and product lifecycles by prioritizing humans.

This study describes the development of an operation-independent simulation model for electrified die-casting foundries which use a smart grid system to cover their energy requirements. The model uses real weather and electricity price exchange data for the simulation period. It can be used to determine and compare electricity costs for production at specific times of day and year, as well as the economic efficiency of different photovoltaic (PV) system and electricity storage variants. It also enables the proportion of different energy sources for each configuration to be analysed. This can be carried out using the model for locations throughout Germany. Additionally, this paper presents exemplary simulation studies that demonstrate the model’s wide range of applications. The results provide an initial overview of the potential savings and optimisation. In the future, the model will provide a basis for determining optimum plant layouts and production times using simulation-based optimisation.

This study demonstrates that hydrogen enrichment in lean-burn spark-ignition engines can simultaneously improve three key performance metrics, thermal efficiency, combustion stability, and nitrogen oxide emissions, without requiring modifications to the engine hardware or ignition timing. This finding offers a novel control approach to a well-documented trade-off in existing research, where typically only two of these factors are improved at the expense of the third. Unlike previous studies, the present work achieves simultaneous improvement of all three metrics without hardware modification or ignition timing adjustment, relying solely on the optimization of the air–fuel equivalence ratio 𝜆. Experiments were conducted on a six-cylinder engine for combined heat and power application, fueled with hydrogen–natural gas blends containing up to 30% hydrogen by volume. By optimizing only the air–fuel equivalence ratio, it was possible to extend the lean-burn limit from 𝜆≈1.6 to 𝜆>1.9, reduce nitrogen oxide emissions by up to 70%, enhance thermal efficiency by up to 2.2 percentage points, and significantly improve combustion stability, reducing cycle-by-cycle variationsfrom 2.1% to 0.7%. A defined 𝜆 window was identified in which all three key performance indicators simultaneously meet or exceed the natural gas baseline. Within this window, balanced improvements in nitrogen oxide emissions, efficiency, and stability are achievable, although the individual maxima occur at different operating points. Cylinder pressure analysis confirmed that combustion dynamics can be realigned with original equipment manufacturer characteristics via mixture leaning alone, mitigating hydrogen-induced pressure increases to just 11% above the natural gas baseline. These results position hydrogen as a performance booster for natural gas engines in stationary applications, enabling cleaner, more efficient, and smoother operation without added system complexity. The key result is the identification of a 𝜆 window that enables simultaneous optimization of nitrogen oxide emissions, efficiency, and combustion stability using only mixture control.

Creative insight is critical to adaptive leadership and innovation in increasingly complex environments. This article introduces Inspirational Intuition as a conceptual framework that integrates intuitive perception and knowledge imagination. Drawing on creativity theory, unconscious cognition, and leadership research, the article outlines four foundational dimensions: Self-Confident Acting, Creative Silence, Emotional Awareness, and Visionary Openness. These form the basis of the IKII-10, a reflective inventory designed for conceptual engagement with intuitive Human Resource Development (HRD) competencies. Rather than an empirical tool, the IKII-10 serves as a scaffold for further theory building, supporting a more holistic understanding of learning, leadership, and organisational development in the face of uncertainty.

Künstliche Intelligenz (KI) kann im Prozess der Leistungsbewertung assistieren und diesen transformieren. Besonders lohnend scheint eine KI-Assistenz bei der Bewertung von komplexem, geschriebenem Text. Jedoch ist der Einsatz von KI im Bewertungsprozess «hochriskant» (EU 2024) und bedarf umfangreicher Analysen. Die vorliegende Studie untersucht, inwiefern ChatGPT-4o die Auswertung und Interpretation von Lerntagebucheinträgen objektiv vornehmen kann. Dafür werden 757 Lerntagebucheinträge aus der geförderten Weiterbildung in Deutschland von Mensch und Maschine bewertet. Sowohl Mensch als auch Maschine erhalten hierzu Kriterien, nach denen die Bewertung vorzunehmen ist; ChatGPT-4o wird diesbezüglich mit einem Prompt unterstützt. Die Übereinstimmung der Bewertungen wird anhand der Masse Sensitivität und Spezifität gemessen. Die Ergebnisse zeigen, dass die Bewertungsvorschläge von ChatGPT-4o eine moderate bis hohe Übereinstimmung mit den menschlichen Bewertungen aufweisen; gleichzeitig neigt ChatGPT-4o jedoch zu einer optimistischen Bewertung der Lerntagebucheinträge. Die Ergebnisse weisen darauf hin, dass eine hybride Intelligenz, also eine Kombination der Stärken von Mensch und Maschine, gewinnbringend für Bewertungsprozesse sein kann. Künftig denkbar sind halbautomatisierte Bewertungsprozesse von Lerntagebucheinträgen, in denen die KI die Bewertung der Lerntagebucheinträge übernimmt und Lehrkräfte bei kritischen Fällen regulierend eingreifen. So könnte die Korrektureffizienz ohne bedeutende Qualitätsverluste gesteigert werden.

The European Union and many governments of its (former) membership states demand more expertise on China, including Chinese language skills. Fostering Chinese language education requires a thorough understanding of the target group. This article aims to lay the foundation for a systematic and comprehensive analysis of the profile of at least one subgroup of Chinese learners, namely adult learners at universities in German-speaking countries. It presents the results of a preliminary meta-study comprising data from 217 Chinese learners. Among the information that can be extracted from various studies are personal data (age, gender, field of study, type of educational institution, first and, where applicable, second language) and data on individual learning biographies (number and specification of languages learned to date, use of learning strategies, and degree of language awareness). Results show that these learners have, on average, a comparable number of previously learned foreign languages. There are more multilingual speakers than, for example, among learners of Spanish. Experienced learners have certain preconditions and needs according to current L3 acquisition theories, which should be paid special attention to when teaching Chinese as a foreign language. Some didactic suggestions are put forward on how to integrate the results into the teaching of Chinese. 

The exponential rise in global plastic consumption, coupled with its heavy dependence on fossil fuels, has resulted in severe environmental and ecological consequences—ranging from greenhouse gas emissions to widespread marine pollution. In response, bioplastics derived from renewable sources have emerged as a promising solution, particularly those synthesized from algae. This study explores the potential of algae-based bioplastics—produced from macroalgae (e.g., Ulva lactuca) and microalgae (e.g., Chlorella, Spirulina) - as a sustainable and circular alternative to conventional plastics. Owing to their rapid growth, high biomass yield, and non-reliance on arable land, algae offer distinct ecological advantages over traditional biomass sources like corn or starch. The research examines the types of algal bioplastics, key production processes, and the integration of genetic engineering techniques to enhance yield and biodegradability. It also addresses scalability, economic feasibility, and degradation by-product limitations. Furthermore, the study evaluates policy and regulatory frameworks that support the development and adoption of bio-based materials. The findings suggest that algae-derived bioplastics reduce reliance on fossil resources and align with circular economy principles by offering biodegradable, renewable, and low-impact alternatives. As global environmental concerns intensify, algae-based bioplastics may play a vital role in reshaping the future of sustainable materials and reducing plastic pollution at scale. 

Learning diaries are reflective tools, often used as formative assessments in adult education with the aim to promote cognitive and metacognitive learning strategies. As grading of and feedback on learning diaries is effortful for teachers, artificial intelligence (AI) may assist teachers in evaluating learning diaries. A prerequisite is that AI's ratings show high accordance with the teachers' ratings. AI accuracy, measured via absolute accuracy and bias, is the focus of the current study with N = 540 learning diary entries focusing on learning strategies, seven teachers, and ChatGPT-4o. Findings revealed that AI evaluations align closely with teacher assessments, indicated by high overall accuracy and low bias. Interestingly, the accuracy varied based on the types of learning strategies assessed in the diaries. Additionally, individual teacher assessments influenced the alignment between human and AI evaluations, suggesting that teachers applied their profession-specific expertise to the assessment process while AI produced somewhat generic evaluations. Overall, the study results indicate that AI can enhance the efficiency of formative assessments while providing timely feedback to learners.

For successful learning, monitoring one's learning behavior and outcomes is essential. While both the use of monitoring strategies and monitoring judgment accuracy are regarded as indicators for metacognitive monitoring, their joint contribution to metacognitive monitoring and their interrelationship is unclear. The current study investigated how monitoring strategies and monitoring judgments (spontaneous as well as prompted judgments) change micro-genetically and influence each other reciprocally during an exam preparation phase. In this diary study, N = 273 higher education students completed knowledge tests and surveys on five consecutive days. Results indicated interindividual differences in dependence of prior performance and gender for the accuracy of prompted judgments but not for monitoring strategies or spontaneous judgments. Random intercept cross-lagged panel models revealed that higher quality monitoring strategies led to the spontaneous formation of monitoring judgments, while the quality of monitoring strategies and prompted judgment accuracy did not influence each other significantly.

Entrepreneurs benefit significantly from resources within their entrepreneurial ecosystems, but under what conditions do they decide to contribute back (a mechanism called downward causation) and thus revitalize the ecosystem they originated from? Drawing on social exchange theory, we develop a set of drivers of such contribution behaviors and test their influence through a metric-conjoint experiment involving 234 entrepreneurs. Our findings confirm the impact of social exchange theory constructs on entrepreneurs' contribution behaviors and highlight the moderating effects of personal traits—in particular, self-interest and other-orientation—on these dynamics. The key insight of our study is that social exchange structures and entrepreneurs’ relational contexts shape contribution behaviors that underlie the microfoundational dynamics of ecosystem development. This investigation underscores the importance of social structures within entrepreneurial ecosystems and enhances our understanding of the micro-level mechanisms that sustain ecosystem health and development. Furthermore, it offers practical insights that transcend traditional policy approaches, focusing on tailored strategies for cultivating entrepreneur-centered ecosystems.

Abstract. Topological Interlocking Elements (TIEs) offer a novel approach to self-supporting and damage-resistant structures, relying on their geometric constraints rather than traditional adhesives or fasteners. While cubic-based TIEs provide ease ways of fabrication and modularity, their limited contact surface areas reduce load transfer efficiency and structural stability. This study introduces a new cubic-based TIE configuration with enhanced contact surfaces, improving interlocking efficiency and mechanical performance. By employing parametric modeling and geometric analysis, the study evaluates the proposed design against conventional cubic TIEs. This research advances interlocking structural systems by presenting a more efficient and stable cubic-based TIE, contributing to improved structural performance and broader applications in engineering and architecture.

Context plays a critical role in talent development, yet most national analyses continue to rely on individual-centered talent concepts. This paper highlights the limitations of traditional models for assessing how countries support talent and proposes a resource-oriented, systemic alternative. Building on the Educational and Learning Capital Approach (ELCA), this study argues that national talent development depends on the availability, accessibility, and orchestration of both endogenous and exogenous learning resources across systemic levels. By analyzing the clumping patterns of excellence in STEM, the arts, sports, and innovation, this paper illustrates the unequal global distribution of talent-supportive environments. Seven key principles for effective resource orchestration are outlined, offering a framework for evaluating and strengthening national talent ecosystems. The paper concludes that systematic assessment and strategic enhancement of national resource landscapes are critical for sustainable talent development and for ensuring that human potential can flourish more equitably across countries.

Dieses essential bietet eine fundierte Analyse und praktische Hinweise zur erfolgreichen Implementierung von Corporate-Influencer-Programmen auf LinkedIn. Es beleuchtet die entscheidenden Einflussfaktoren, die Mitarbeitende motivieren, als digitale Markenbotschafter für ihr Unternehmen zu agieren und zeigt auf, wie Unternehmen diese Potenziale optimal nutzen können.

In einer detaillierten Conjoint-Analyse werden die Rahmenbedingungen für erfolgreiche Programme untersucht – von besonderer Bedeutung ist dabei eine moderne und gelebte Unternehmenskultur. Für alle Verantwortlichen in der Unternehmenskommunikation, HR und Marketing, die ihre Strategien im Bereich Corporate Influencing optimieren und langfristig eine positive Arbeitgebermarke aufbauen wollen.