The digital transformation of agriculture has accelerated the adoption of data-driven technologies such as IoT sensors, satellite imagery, blockchain, and artificial intelligence. However, small and medium-sized farms often face structural challenges related to data governance, interoperability, and equitable value creation, limiting the sustainable use of digital innovations. This paper proposes a farmer-centric data governance and analytics framework that integrates heterogeneous farm data sources into a socio-ecological value assessment system. The framework combines low-cost data collection tools, including IoT sensors, mobile applications, cooperative surveys, and administrative records, with secure data processing pipelines and traceable storage mechanisms. At its core, a socio-ecological value scorecard (SEVS) aggregates ecological, social, and economic indicators to support balanced decision-making that aligns short-term productivity with longterm sustainability. The framework operationalizes governance principles such as transparency, shared responsibility, and data sovereignty through practical policies, cooperative stewardship, and accessible technical mechanisms. The proposed model contributes to smart agriculture research by demonstrating how governance-aware data architectures can enable inclusive, trustworthy, and sustainable digital farming practices.

Digital competence is a critical skill for teachers in the modern era, as technology reshapes the education ecosystem. The ability to integrate digital tools effectively into teaching practices not only enhances learning outcomes but also equips students with skills that are important for their daily lives. For teachers, fostering digital proficiency is fundamental to addressing the challenges of contemporary education environments, promoting inclusive learning, and addressing the evolving demands of the digital age. This article evaluates the digital competencies of K1-12 teachers in Kosovo using the DigCompEdu framework, providing information on six competency areas in the Digital Competence for Educators Famework (DigCompEdu). Using the SELFIE tool, data were collected from 441 in-service teachers across Kosovo and analyzed through normality and reliability tests. The Kolmogorov-Smirnov (KS) and Shapiro-Wilk (SW) resulted in significant deviations from normality; therefore, the analysis proceeded with non-parametric methods, primarily using the Kruskal-Wallis test for group comparisons. The reliability resulted in strong internal consistency with a Cronbach’s Alpha value of 0.901. The results reveal varied proficiency levels, with the highest scores in the “Digital Resources" domain and the lowest in "Teaching and Learning". Age and experience with digital tools significantly influenced certain competencies, highlighting disparities between demographic groups. By aligning DigCompEdu with the TPACK framework, this research underscores the need to bridge the "what" (skills) with the "how" (application) in developing digital competence for effective integration by teachers.

Artificial Intelligence (AI), and particularly Large Language Models (LLMs), is reshaping healthcare by enabling more efficient, accurate, and scalable solutions for complex administrative tasks, including diagnosis coding, which remains a predominantly manual process that is time-intensive and prone to human error. Limited studies have addressed the use of AI in facilitating diagnostic coding within specialized clinical environments. To address this gap, this paper employs a qualitative participatory design science research (DSR) approach to explore the subjective experiences, needs, and expectations of healthcare professionals involved in diagnosis coding. A co-designed LLM-based prototype was developed to improve efficiency, transparency, and accountability. Data were collected through observations, interviews, and workshops, and analysed using thematic analysis, which guided the design process. Findings revealed that manual diagnosis coding is time-intensive, inconsistent, and relies on inefficient paper-based references. Participants emphasized the need for an AI-automated system that prioritizes explainability, confidence indicators, and system integration. The co-designed prototype with a dual-panel interface was validated for its simplicity, efficiency, and alignment with existing clinical routines. This study advances the understanding of human-AI collaboration in healthcare administration while providing practical guidance for implementing integrated, explainable systems that transform manual coding to quality validation in AI-assisted diagnosis coding workflows.

This paper explores the co-creation process between industry and academia in the development of courses within Interaction Design and Artificial Intelligence. Through a collaborative workshop with industry representatives and a survey targeting current students and alumni, we gathered insights into their needs, expectations, and priorities for future educational offerings in these fields. The co-creation process resulted in the development of several new course proposals, which were assessed by students and alumni coming from two bachelor programs. The findings present six courses with descriptions alongside their ratings. We also present qualitative feedback from students and alumni regarding their perceptions and anticipated value of these courses. The study highlights the importance of engaging industry stakeholders, students and researchers in curriculum development to ensure academic relevance and industry alignment. This work contributes to ongoing discussions on collaborative curriculum design in higher education, particularly in fields undergoing rapid change.

The furniture industry is increasingly compelled to adopt sustainable practices and transition towards a circular economy due to complex supply chains and significant waste generation. The Digital Product Passport (DPP) is presented as a critical tool at the nexus of sustainability and digital transformation, designed to provide accessible and trustworthy information about a product’s lifecycle. This exploratory qualitative study aims to understand how DPPs can be successfully integrated into the furniture industry’s digital transformation journey to enhance sustainability and lifecycle management. Thematic analysis of the gathered data revealed five key themes: the necessity of a digital strategy, the impact of regulatory compliance, the role in supporting the circular economy, challenges related to technical infrastructure, and the foundational importance of traceability. Findings indicate that successful DPP implementation is strongly linked to having a robust digital strategy and appropriate technological infrastructure, driven significantly by growing regulatory requirements, while actively facilitating circular economy principles.

Complex and wicked problems characterized by high levels of uncertainty motivate the use of multimethodology (or methodological pluralism) in which different methodologies and methods are combined to better approach problems and develop solutions. This methodological pluralism has been applied across disciplines, including the systems thinking field, where scholars employed complementary methodologies and methods. Idealized design and interactive planning represent one such systems methodology that offers a structured action-oriented approach to redesigning a system toward an idealized state rather than attempting to predict it. Although conceptually well-established, idealized design has been noted for its limited practical guidance, particularly in identifying where and how to intervene in a system. The present paper addresses this gap by proposing an enriched idealized design framework that integrates rich pictures from soft systems methodology and causal loop diagrams, system archetypes, and the leverage points framework from system dynamics. Each tool is integrated at a specific phase of the idealized design process, forming a progressive cascade while preserving the original sequence of the idealized design approach. A retrospective empirical demonstration drawing on a longitudinal study on computational thinking (CT) education is presented to illustrate the conceptual coherence of the framework, and potential directions for future work, including prospective validation, are discussed.

The generation and verification of academic credentials, particularly diplomas, is crucial for the secure functioning, enhanced legitimacy, and confidentiality of higher education institutions (HEIs). Digitising the internal services of HEIs is of great importance, as it mitigates the potential abuse of diplomas and other important documents. The prevalence of forgery attempts highlights the need for robust verification methods. In this regard, blockchain technology can be considered as a solution due to its capability to ensure the immutability, decentralisation, security and transparency of all documents stored on the blockchain network or storage. In this paper, we aim to move from concept to practical implementation of a blockchain-based framework for generating and verifying diplomas, named Diploma Integrity Authentication Record (DIAR), which, in addition to the digitalization of diplomas and academic documents, also enables their fast, secure, and cost-effective verification. Through the paper, we describe in detail the process of practical implementation of the DIAR framework, presenting the smart contract for generating and verifying diplomas, the process of encrypting and storing diplomas as non-fungible tokens on the blockchain network or storage. At the same time, the challenges of practical implementation are also described, which are predominantly related to the scarcity of blockchain solutions for generating and verifying diplomas in HEIs. Significant attention and emphasis are placed on the legal matters pertaining to the implementation and safeguarding of students’ identification and data privacy.

Computational thinking (CT) is a critical thinking skill and an essential component of digital competence frameworks for problem-solving. Kosovo's education system represents an emerging context where digital integration and CT initiatives are underway yet face systemic implementation challenges. This study examined Kosovo teachers' perceptions of CT integration and identified strategic intervention points using the leverage points framework from systems thinking. Through mixed-methods analysis of survey data and focus group discussions with teachers, current and ideal CT integration were mapped across leverage point levels. Findings revealed moderate CT knowledge among teachers and positive attitudes toward its cross-curricular integration, yet they faced barriers including content-heavy curricula, rigid assessment structures, and inadequate professional development. Systemically, an intervention hierarchy was modeled with different effectiveness levels, ranging from low-leverage areas such as class size and infrastructure to high-leverage areas such as shifting the system's goal toward competency-based education. This teacher-informed typology provides a practical diagnostic tool for digital education reform in similar contexts. The study offers three key implications. Theoretically, the study operationalizes the leverage points framework through empirical data. Practically, findings prioritize competency-focused assessment and teacher autonomy. For policy, the framework recommends cross-disciplinary CT curricula with structural reforms fostering teacher self-organization in resource-constrained contexts.

Agriculture, vital for sustenance and economic stability, faces growing pressures from resource scarcity, urbanization, and a surging global population. Achieving sustainable food production necessitates integrating state-of-the-art technologies within a complex, diverse ecosystem encompassing farmers, businesses, and policymakers. A socio-ecological value scorecard is essential for informing decision-making and enhancing farming systems; yet, there is a significant gap in thoroughly examining its role in addressing agricultural challenges. This paper addresses this by presenting a systematic literature review (SLR) of 26 relevant studies, rigorously selected from an initial 1010 papers published between 2014 and 2024. Articles underwent a two-phase screening process based on specific inclusion and exclusion criteria, followed by a quality assessment criteria to ensure reliability and relevance. The review highlights the critical role of a socio-ecological value scorecard to evaluate the broader implications of agricultural activities, guiding stakeholders toward more sustainable and responsible practices. This review also explores opportunities and challenges in leveraging data-driven agricultural innovations, precision agricultural techniques, the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) to enhance control, productivity, and sustainability. It emphasizes the importance of data governance and sharing to unlock the full potential of these technologies, moving beyond traditional, experience-based methods. Moreover, the review provides a comprehensive analysis and taxonomy, illustrating the current research landscape and offering insights for future studies in developing and implementing a socio-ecological value scorecard for sustainable smart agriculture. Ultimately, the study contributes to understanding how a holistic, data-driven approach can pave the way for responsible and environmentally conscious farming practices.

"Artificial Intelligence (AI) in Healthcare Information Systems: Security and Privacy Challenges” offers a deep dive into the integration of AI in healthcare, with a primary focus on addressing the significant security and privacy concerns that arise in this domain. The chapters in this book highlight the transformative potential of AI in diagnosing and predicting diseases, as well as its impact on fields like fetal medicine, but places special emphasis on the need for robust encryption, data protection techniques, and ethical considerations to safeguard sensitive healthcare data. The book also explores global case studies, from India to Kazakhstan, outlining the challenges and prospects of AI adoption in diverse healthcare settings. Readers will gain insights into AI's role in improving patient outcomes while navigating the complexities of data privacy and security. The book is a valuable resource for healthcare professionals, technologists, and policymakers who are focused on implementing AI-driven solutions securely and ethically in healthcare systems.