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

This vision paper outlines a future learning ecosystem in which AI agents act as tutorial companions, analyse learning progressions and make adaptive recommendations. The use of retrieval augmented generation (RAG) systems will make learning content accessible in a contextualised manner and generate individual feedback. Initial prototypes from the corporate world demonstrate the technical feasibility of this. We therefore assume that if AI-supported agents are successfully trialled, everyday teaching and learning at universities can be fundamentally changed in the direction of self-directed learning.

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.

The building sector accounts for almost 1/3 of the global final energy consumption, which is mainly made up of heating and cooling, as well as domestic hot water heating, lighting and household appliances. Due to global warming, the energy demand in buildings for heating will decrease, while that for cooling will increase in future (V. Ciancio et al., Sustainable Cities and Society 60, 102213 (2020)). Energy-saving ventilative cooling strategies like e.g. night cooling or hybrid ventilation in mixed-mode buildings therefore will be favourable compared to conventional heating, ventilation and air conditioning (HVAC) systems (L.L. Gomis et al., Energy and Buildings 231, 110597 (2021)).

Our research focuses on evaluating the effectiveness of automated, window-integrated ventilation systems compared to conventional ventilation and air conditioning systems for thermal control during summertime. We use two floors of an office building as a real laboratory for this purpose. Extensive data is collected via KNX sensors installed in the building. The study evaluates scenarios with manual and controlled natural window ventilation, decentralised window-integrated mechanical systems with heat recovery and combinations of all of them. The accuracy of the KNX sensors is checked with the help of laboratory measuring devices.

By connecting to a robust database, we capture detailed environmental metrics including temperature, humidity and CO2 levels, and other parameters such as user presence. Statistical and AI-supported analyses are used to determine the comfort level, user acceptance, energy requirements, CO2 emissions and economic costs for different scenarios. The results are compared with established building performance simulation tools such as EnergyPlus or IDA ICE.

Through detailed analyses and simulations, including user behaviour and the influence of user-specific conditions, our comprehensive approach will provide a better understanding on the optimal dimensioning, configuration and operation of natural, mechanical and hybrid window-integrated ventilation systems, improving operational efficiency in terms of cycles and running times. The study emphasises the need for sensor-based parameter recording to improve the accuracy of demand forecasts and thus support the efficient and sustainable use of energy resources and provides actionable recommendations for optimizing summer thermal control through automated, window-integrated solutions.

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.