Traditional models of interdisciplinary collaboration in higher education often struggled to transcend disciplinary silos, resulting in fragmented Communication and superficial integration. The Components of Role-playing in Experiential Learning (CREL) Framework offered a transformative alternative by embedding role-playing mechanics into structured learning environments, enabling participants to embody perspectives beyond their disciplinary boundaries. First, this paper introduced the five core components of the CREL Framework—Scenario, Social Contract, Collaboration, Choice, and Experience—and explained how they function as an integrated system for designing and assessing collaborative learning environments. Then, it argued that the framework catalyzed effective interdisciplinary collaboration and authentic experiential learning by simulating complex, real-world scenarios that demanded mutual understanding, adaptive thinking, and co-constructed solutions. Next, drawing from theoretical foundations in ludology, experiential education, and transdisciplinary Communication, the authors demonstrated how CREL facilitated role adoption, dialogic engagement, and reflective inquiry. Finally, by positioning play as a rigorous epistemic practice rather than a pedagogical supplement, the article advanced a more inclusive, participatory, and dynamic vision of interdisciplinary education. It concluded by outlining the theoretical foundations of the framework, addressing key limitations, anticipating common critiques, and calling for curricular, institutional, and policy reforms to legitimize role-play as a mode of scholarly engagement and professional development.

Drone swarms are groups of unmanned aerial vehicles (UAVs) that operate in a coordinated manner to complete their assigned tasks. The coordination logistics are inspired by biological collectives such as flocks of birds or swarms of insects. Unlike individual drones, swarms leverage numbers and coordination to accomplish complex tasks more efficiently, robustly, and with greater adaptability. Artificial intelligence (AI) plays a pivotal role in elevating drone swarms from mere programmed machines to intelligent agents capable of self-organization, decision-making, and rapid response to dynamic environments. AI enables swarms to operate with minimal human intervention and adapt to changing scenarios in real-time. This paper focuses on the applications of artificial intelligence in making the operations of drone swarms as efficient and as smooth as possible.

The field of engineering education faces an ongoing challenge of effectively preparing students for the demands of the rapidly evolving technological landscape. Traditional engineering curricula often focus more on theoretical knowledge rather than practical application and outcomes. As a result, engineering graduates are not equipped with the necessary skills to excel in industry, leading to a gap between industry requirements and graduate competencies. To bridge this gap, a results-driven curriculum for engineering education has been proposed to provide students with the knowledge, skills, and abilities required to succeed in industry. To address the question, "Where do most curriculum programs fall short?" It is noted that curriculum programs focus on delivering content rather than delivering experiences that support and enable change in teaching and learning. When curriculum design is driven by content, it ends up with numerous blurred boundaries regarding the scope, audience, and the applicability of what is contained in the curriculum. Consequently, the curriculum may result in education that falls short in producing graduates required by current and future markets and industry. In an e-learning and distance education environment, a curriculum might look well designed and meet all the set criteria, but it might ultimately match the wrong objectives. The result of this mismatch is that students after completing the curriculum depart with a lot of information in their heads but with no practical skills that they can implement in the workplace. This indicates that the graduate attributes required by Engineering Council of South Africa (ECSA) and industry were not attained by the student at completion of the enrolled curriculum. The research adopts a qualitative case study approach to explore and explain the steps, activities, and tools that can be used to design a results driven and engineering-focused curriculum solution, that has a clear goal tied to well-articulated pedagogy strategy.

Artificial Intelligence (AI) is being used increasingly worldwide to provide better legal education to the public by providing them with easily accessible and automated legal information. However, its integration into justice systems raises significant legal, ethical, and policy concerns. In Albania, where legal literacy remains low despite ongoing judicial reforms, AI-driven tools present both an opportunity and a challenge. This study explores the legal and regulatory implications of incorporating AI into public legal education, analyzing potential risks related to misinformation, algorithmic bias, data privacy, and human rights compliance. Using a doctrinal legal research approach, the study examines Albania's existing legal framework, including constitutional provisions on access to justice, data protection laws, and the justice reform strategy and subsequent legislation. This is followed by a comparative legal analysis of AI-driven legal education initiatives in Estonia, the United Kingdom, Canada, and Singapore. This analysis provides insights into regulatory best practices. Furthermore, the study evaluates AI's alignment with international human rights norms, particularly the right to legal information under UN Sustainable Development Goal 16. The findings of this study reveal the existing gaps in Albania's legal system regarding AI-driven legal education and emphasize the need for including strong legal protections. The study proposes policy recommendations aiming at the usage of AI tools to enhance public legal literacy while maintaining legal accuracy, transparency, and accountability. These recommendations include AI oversight mechanisms, legal accuracy standards, and ethical AI guidelines that are tailored to Albania's socio-political context. The responsible integration of AI capabilities into legal education can help Albania improve public trust in its justice system and strengthen democratic participation.

The application of artificial intelligence in animal husbandry and veterinary medicine is gaining increasing attention. Using computer vision systems for assessing animal welfare seems promising in the latter field. The Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe) is developing systems for assessing animal welfare based on innovative technological tools leveraging deep learning algorithms for complex computer vision tasks. These tools enable the automation of data processing, significantly increasing efficiency and scalability. By replacing labor-intensive manual analysis, the system allows for the rapid processing of large volumes of data, ensuring the extraction of critical information that would otherwise be lost or impractical to obtain through conventional methods.

This study uses Random Forest algorithm to model students' final year mark in an engineering technology module taught by the University of South Africa. The algorithm uses a supervised learning classification technique to map the different assessment marks and the final mark. Hence, the latter are labelled instances whereas the former constitute the features. Random Forest (RF) has been applied to Structural Analysis 3, which takes into consideration the graduate attribute concept or level of competence as far as assessments are concerned. Firstly, the RF is subjected to imbalanced binary classes, then balanced classes are achieved by Synthetic Minority Oversampling Technique (SMOTE) and class weights adjustment techniques. The results showed that SMOTE brought an improvement in accuracy of 3%. It was also revealed that an increase of 4, 15 and 9% in precision, recall and F1-Score were observed in predicting non-competent students. An increase of 4 and 3% was noticed in the case of the precision and F1-Score respectively in predicting competent students, whereas the recall did not display any change. Despite the RF with SMOTE overperformed standard RF and RF class weights adjustment, all three algorithms were good candidates in the prediction of student performance. RF-SMOTE could be suggested as a guiding instrument when dealing with imbalanced data.

The emergency (warfare-induced) and sustainable (pandemic-induced) digitization changes in the higher education sphere of Ukraine heralded the enhancement of pervasive dimensions of learning – digital, hybrid, and blended, synchronous and asynchronous, AI-enhanced. This end-to end digital shift in the educational processes (communication, content, outcomes and outputs, skills) introduced the meta-disciplinary dimensions of learning, mitigated by human-in-the-loop and AI-in-the-loop formats of educational communication. These meta-disciplinary dimensions can be considered conduits of vertical (endocentric) and horizontal (exocentric) transdisciplinary of digital education as a sustainable dynamic system. Applied trans-disciplinary lens contributes to the solution of holistic modeling of processes and results of updating models and mechanisms of the highly dynamic communication system of education in the digital environment as a whole and its individual formats in the emergency digitization measures of different types to foster resilience and sustainability.

This research explores the crucial role of bridge makers in fostering the success of open innovations within innovation ecosystems. Emphasizing governance structures, this study highlights the importance of creating synergies among participants through effective intermediaries. Bridge makers act as connectors, facilitators, and integrators, crucial for fostering collaboration among diverse stakeholders, aligning interests, and overcoming barriers to innovation. By integrating the bridge maker role into ecosystem strategies, stakeholders can ensure that the connections formed are not only numerous but also robust, inclusive, and capable of driving long-term innovation and collaboration. Given the fragmented nature of recent literature on the role of bridge makers, and the various terminologies used to describe similar roles, this research aims to provide a clear definition and comprehensive understanding of the bridge maker's role. The objective is to analyze different aspects and names attributed to this role within the context of innovation ecosystems. This paper concludes by discussing future research avenues that can build on the developed role typology, shedding further light on the process of open innovation ecosystem genesis. By incorporating the bridge maker role into the various ecosystem models, this research suggests that enhanced connectivity and synergy can be achieved, benefiting the entire ecosystem.

There are several popular note-taking methods, amongst which the Cornell note taking method is very well-known. The method consists of taking notes by dividing the page into three parts: the main notetaking part; the part summarizing themes, key-, and cue- words; and a part summarizing, in a few lines, contents. This article explores using the Cornell method, originally designed for students, as a means of pedagogy delivery for instructors. Hendel's four pedagogic pillars, rooting pedagogic excellence in fundamental psycho-neurological processes, explain the success of the Cornell method as attributed to its encouraging executive function and optimal goal-setting techniques. The paper also examines the contrastive practices of some textbooks and confirms critiques of these approaches as employing lower levels of the Bloom- Anderson hierarchy.

Although there are several frameworks for the synthesis of form, and for the automated development of geometric shapes, studies in the systematic analysis of architectural forms and their automation are still underrepresented. This paper describes a framework for computer-aided geometric form analysis. It illustrates the framework's applicability by an example from precedent architectural design. The form analysis framework is developed theoretically and further translated into a computer code that is written in the AutoLISP language to fit its graphic operation system. Application of the framework and its computerized code demonstrates its power as a promising analytical tool that can help designers analyze precedents' morphological structure more efficiently.