Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Artificial intelligence (AI) and digital technology provide extensive possibilities for education. But focusing only on their implementation does not address the challenges associated with them and they may even have a negative impact on learners. So far, the disciplines of psychology and computer science have not provided a theoretical framework for the competence needed to develop and use AI and digital technology in education in order to prepare learners for successful participation in modern societies. The main aim of this paper is to theoretically specify competent use of AI and digital technology in education and to provide a standardized instrument to evaluate courses that teach this competence. Therefore, we (a) combine theoretical contributions from both scientific disciplines to formulate a theoretical framework with four levels for the “Competence to Use Artificial Intelligence and Digital Technology in Educational Processes” (AIEDTEC competence), (b) introduce a questionnaire to evaluate courses that teach AIEDTEC competence, and (c) present results regarding its psychometric properties (N = 240). The questionnaire showed good to very good psychometric properties and the assumed factor structure was supported by confirmatory factor analyses. The paper connects research on systems thinking and learning and instruction with recent developments regarding AI and digital technology and thereby provides an essential base for creating effective, modern, and safe learning environments in the future as well as a psychometric evaluation instrument....
Islanded microgrids face considerable operational difficulties because of the inconsistency of renewable energy sources and ongoing dependence on diesel power. This study offers a comparative assessment of a traditional rule-based energy management system versus an AI-augmented energy management system for a hybrid island microgrid that includes photovoltaic generation, wind generation, battery energy storage, and diesel generator. The suggested AI-driven controller incorporates short-term predictions and heuristic scheduling to enhance dispatch choices. Simulations usingMATLAB and Simulink Ver-sion 25.2.0.2998904 (R2025b) over a 24 h period show enhanced management of battery state-of-charge, decreased operation of the diesel generator, and greater use of renewable energy. The findings show a decrease in fuel usage and carbon dioxide emissions of around 63% in comparison to the baseline rule-based approach....
Understanding the complex relationships between processing conditions and mechanical properties in aluminum alloys remains a critical challenge in materials science. This study presents a data-driven framework using explainable artificial intelligence to quantify and interpret how different processing routes influence the strength–ductility trade-off in aluminum alloys. Using a comprehensive dataset of 1154 aluminum alloy samples with 10 distinct processing conditions, optimized XGBoost models were developed via Bayesian hyperparameter tuning to predict yield strength (R2 = 0.9392), tensile strength (R2 = 0.9491), and elongation (R2 = 0.6767). The strength models showed high predictive accuracy, whereas elongation showed lower and less uniform reliability, with the largest relative errors in the 0–5% elongation regime. SHAP (SHapley Additive exPlanations) analysis revealed that processing condition is the most influential feature for yield strength prediction, while Cu dominates tensile strength prediction. True SHAP interaction analysis identified Processing_encoded interactions with Cu as the strongest processingcoupled contribution, followed by Mg and Al, with Zn, Si, and Li showing smaller but non-negligible interaction contributions. The decision-tree surrogate is presented as an exploratory rule-extraction tool rather than as a standalone processing-selection classifier. These findings demonstrate that explainable Machine Learning (ML) can support interpretation of processing–property relationships in aluminum alloys when predictive limitations, class imbalance, and the associative nature of SHAP explanations are explicitly considered....
The adoption of artificial intelligence (AI) tools for hospital insulin management is currently limited by data fragmentation and difficult integration into clinical workflows. This commentary examines the data infrastructure requirements for safe AI deployment in clinical settings. Device-mediated and clinician-administered dosing are the two methods by which insulin is managed in hospitals. In device-mediated dosing, glucose and insulin data often remain siloed within proprietary device ecosystems outside the electronic health record (EHR). In clinician-administered dosing, relevant data elements typically exist within the EHR but are distributed across workflows in ways that limit their usefulness for decision support. The Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE) initiative is a standard for integrating device-generated diabetes data into clinical systems, which can lay the foundation for organizing hospital data in support of the development of trustworthy AI. A staged roadmap for hospitals building towards AI-ready insulin management infrastructure is presented along with governance requirements for trustworthy deployment. The value of iCoDE is that it helps define the conditions under which such AI can become clinically meaningful, trustworthy, and scalable....
1. The use of camera traps in ecology and conservation has expanded rapidly, but the time spent to accurately identify species in camera trap images remains a fundamental challenge that limits project scope and impact. Although artificial intelligence (AI) is often used to speed up image processing, a human review step is still standard practice to arrive at final species identifications. A potentially transformative next step is thus to remove humans entirely from the analysis chain and still produce accurate statistical models for subsequent inference across a wide diversity of species and regions. 2. Here, we compare the output of Bayesian multi-species occupancy models derived from a complete AI workflow (no human review of images) with a general species classifier (SpeciesNet) to those from an expert (human) workflow, using large-scale camera datasets from three study areas and two distinct and diverse mid-large mammal assemblages. We apply several pre-and post-processing steps to the AI workflow to improve model agreement. We perform a comprehensive model comparison, assessing agreement in identified species–environment relationships and rates of occupancy and detection, and similarity of spatial projections of occupancy. 3. We found that for most species of mammal, AI based models were remarkably similar to expert-based models, with some variability based on post-processing decisions. This agreement was robust, holding across multiple metrics of comparison (e.g. parameter estimates, precision, occupancy and detection rates), multiple study sites and at species and community levels. Similarity in model output occurred even in the presence of misclassification errors, suggesting our approach was resilient to some level of false negatives and positives. Substantial divergence in model output and subsequent inference, while rare, was most prevalent for rarely detected species. 4. Synthesis and applications. The use of a global AI classifier to identify species and reproducible pre-and post-processing decisions makes our approach broadly applicable and particularly beneficial for national and international monitoring programs that collect large amounts of photo data on threatened, at risk, or management sensitive species and wildlife communities. A fully automated workflow will allow such programs to progress more rapidly from photo collection to analysis, inference and decision-making....
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