Artificial intelligence (AI) is rapidly transforming higher education by enhancing instructional processes, improving administrative efficiency, and enabling personalized learning experiences. However, it also introduces significant risks related to privacy, authorship, academic integrity, and institutional accountability. As adoption accelerates, universities face an urgent need to establish clear, campus-wide governance structures that define responsible use, protect institutional knowledge assets, and ensure ethical implementation. This study examined this dual-use dilemma through a conceptual document analysis, systematically reviewing and synthesizing institutional policies, academic literature, and global governance frameworks. These sources were compared to identify cross-framework patterns, recurring institutional gaps, and practical requirements for the responsible adoption of AI. Findings reveal weaknesses in academic integrity guidelines, data governance controls, authorship standards, literacy training, and mechanisms for monitoring AI-enabled systems. In response, this study introduces the SPARKE Framework, a six-component model that translates ethical principles into operational safeguards by integrating policy development, privacy protections, integrity rules, literacy initiatives, knowledge management processes, and enforcement mechanisms. The framework contributes to applied knowledge management by offering institutions a structured roadmap for aligning innovation with accountability. By emphasizing transparency, oversight, institutional learning, and responsible use, the SPARKE Framework provides universities with a scalable approach for managing AI across academic and administrative environments. It highlights broader implications for academic integrity, institutional trust, and the development of sustainable governance infrastructures that support responsible AI adoption.
Knowledge Hiding is the intentional withholding of knowledge from colleagues, often driven by a lack of trust. It manifests as rationalized hiding, evasive hiding, or playing dumb; the latter two are particularly detrimental to workplace trust. Organizations invest in promoting Tacit Knowledge and Explicit Knowledge sharing to enhance teamwork, problem-solving, and workplace relationships. Recent studies emphasize the importance of understanding the characteristics of Tacit Knowledge and Explicit Knowledge, as well as the motivations behind Knowledge Hiding and Knowledge Hoarding. This study aimed to identify key factors influencing individuals' decisions to share Tacit Knowledge or engage in Knowledge Hiding and Knowledge Hoarding. Using an 11-stage Survey Design methodology, data were collected from 285 Knowledge Management professionals across five countries in North America and Europe. Participants represented a diverse range of industries and organizational sizes, providing a broad perspective on knowledge behaviors. Results indicated participant awareness of and engagement in Knowledge Hiding, Knowledge Hoarding, and Knowledge Sharing. They recognized that Tacit Knowledge holders made deliberate sharing decisions based on factors like trust, sincerity, and expertise. Additionally, organizational culture, incentives, and perceived risks played a role in these decisions, with some respondents highlighting the impact of competitive environments and personal career advancement. The study highlighted the need for future research to include leadership influences on Knowledge Hiding, Knowledge Hoarding, and trust and distrust in the workplace.
Open Government Data (OGD) has become a key driver of transparency, innovation, and sustainable development. By making public sector information accessible, OGD fosters economic growth, improves public services, and strengthens democratic governance. This paper explores the role of OGD in promoting innovation and advancing the Sustainable Development Goals (SDGs), particularly in urban planning, environmental protection, and public health. It also examines challenges in data accessibility, interoperability, and usability, highlighting the need for standardized formats and open APIs. A case study of the JakDojade transport planning application demonstrates how OGD can enhance smart mobility, reduce environmental impact, and create business opportunities. However, limitations in data availability and licensing practices hinder full utilization. This paper calls for improved data-sharing policies and IT infrastructure to maximize the societal benefits of open data.
Organisations’ conscientious activities and their members’ knowledge management endeavours reflect the value of knowledge in meeting organisational goals. This research aimed to determine the effect of workplace belongingness, horizontal solidarity, and concern for others on knowledge-sharing behaviour among university academics and whether the effect is moderated by self-efficacy in knowledge-sharing. In this investigation, a quantitative methodology and a cross-sectional design were implemented, using a self-administered survey to collect data from 324 university academics. The questionnaire was prepared using a five-point scale. Participants comprised 38% women, 86% married and a mean age of 39.34 years (SD = 7.87). A regression statistical test, accompanied by Hayes’ PROCESS Micro, was adopted for analysis. The analyses revealed workplace belongingness, horizontal solidarity, and concern for others as positive predictors of knowledge-sharing behaviours among university academics and that knowledge-sharing self-efficacy was not a moderator in the relationships. This study also found that changes in the three independent factors accounted for 29% of the variance in knowledge-sharing behaviour. Workplace belongingness explained the greatest fluctuation in knowledge-sharing behaviour between predictor factors. Taking into account the predictor variables of this study, interventions are recommended to encourage knowledge-sharing behaviour among university academics.
Artificial intelligence (AI) is driving massive changes in education, with uses growing for educators and students. A significant portion of students embrace AI, but little is known about how narrow or wide-ranging their view of the benefits and disadvantages of AI is, especially in international settings. We report a survey of 91 Romanian computer science students, showing that knowledge of AI is associated with reporting the usefulness of AI. Those with higher AI knowledge were more likely to read books and papers on AI, report that AI will impact art, and actively seek information on AI. The most frequently mentioned source for learning about AI was the internet (81%), and the feeling about AI was mostly curious (68%). The most frequently cited advantage of AI was its ability to solve problems, and the most frequent disadvantage was the cost. Less than a third cited strong disadvantages of AI in education. These findings highlight student openness to using AI, especially as they know more about it. These findings also indicate that students may need coaching about the disadvantages of AI in educational settings.
A Security Operations Center (SOC) is an indispensable tool for any modern organization or enterprise to secure its digital data and information assets. Developing SOCs and SOC capabilities to meet organizational needs in today’s threat environment is an often laborious, time-consuming, and expensive task that (if not done correctly) may leave organizational goals unfulfilled. In this paper, we introduce the Ontology for SOC Creation Assistance and Replication (OSCAR), which organizations can use to aid in developing SOCs and in planning and evaluating SOC capabilities. We developed OSCAR using a purpose-built dataset created by extracting the knowledge of numerous SOC expert practitioners. OSCAR is organized into a knowledge hierarchy that includes people, process, and technology classes, but also emphasizes planning and functional considerations. OSCAR accomplishes two things. First, it fills a gap in existing cyber ontology literature by including classes for the initial development of SOCs in addition to those for security operations capabilities. Second, its domain-specific knowledge is derived from a unique dataset gathered directly from experts working in the field. Taken together, these unique traits make OSCAR an ideal tool for planning, building, and evaluating SOCs.
Digital Humanities (DH) represent a pivotal evolution in the integration of current technologies, especially Artificial Intelligence (AI) and particularly Generative AI (GenAI), within humanities research. Due to the interdisciplinary nature of DH and its interfaces with computational methods, this paper examines the penetration of DH into Computer Science (CS) publications over the years. This research applied a culturomics approach, using specific search queries in the dblp (computer science bibliography) database to track the emerging trends of DH-related terms in CS research publications from 1990 to 2024. Findings show Cultural Heritage (CH) is being dominant compared to all other DH terms since 1994, while DH has a consistent rising trend since 2006 within CS publications. The study reflects the need for knowledge sharing and joint expertise between researchers to create new knowledge. The implications of this study encourage greater openness of academia to cross-disciplinary publications that may lead to a broader understanding of complex societal and cultural issues
Information systems are enablers, and organizations struggle to implement and manage them. Many organizations experience insufficient coordination of work, knowledge sharing, and use due to a business-information technology gap. As the top echelons responsible for information systems decisions, university information systems executives and the top management team form the strategic information systems leadership. The study aims to interpret perceptions of strategic information system leadership towards information system function performance in universities in Kenya. The data were collected from 42 out of 76 public and private universities. A qualitative survey approach was used to collect the data. A thematic approach was used to analyze and interpret the data. Microsoft Excel was used to organize the data, coding, and analysis. From the findings, strategic information systems leadership is perceived to positively affect information system function performance. The study also found that Kenyan universities lack adequate information system resources and operate with minimal budgets. Sharing of information systems knowledge was found to be limited, negatively affecting information systems culture and performance. Management practices that promote shared leadership were inadequate. The study recommends continuous training for university strategic leaders, making information systems executives part of the top management team, enhancing shared understanding between strategic team members, and adopting practices that build social interactions and promote knowledge sharing and integration. This research will help universities to examine their information system function policies, culture, and available capabilities.
A prominent medical school in the Southeast United States conducts an active International Medical Outreach Program (IMOP) at multiple international locations. These medical outreach trips provide medical services to those who do not have access to or limited access to health care. Medical students who volunteer to participate in these trips welcome the experiential education experiences. This developmental research study focuses on three IMOP trips to San Cristóbal, Galápagos Islands (Ecuador). Traditional approaches in the dissemination of clinical knowledge, skills, and confidence building for medical students rely on classroom training. In the IMOP experiential trips, knowledge sharing was supported by oral communication with senior clinicians and patients, as well as the transition from a paper-based to a novel custom-built Electronic Health Record (EHR) system. Paper-based record systems are cluttered with issues; some include illegible handwriting and missing or incomplete data. A team of health informatics professionals from the university transitioned the paper-based forms to a novel digitalized EHR system. The new novel digitalized EHR system resolved many of the paper-based issues and significantly improved knowledge dissemination among medical students, patients, and clinicians, furthering skills enhancement as well as efficiencies in resource coordination, however, some adjustments in clinical documentation procedures were needed to reduce the volume of missing data entries during the implementation, where a total of 848 adult and pediatric patients were treated over the three IMOP trips documented in this research. This digitalized EHR system enabled a more efficient transfer of knowledge to medical students.
The Internet of Things (IoT) technology has revolutionized how businesses operate and changed our daily lives. IoT devices are used in different areas, including smart cities, smart agriculture, smart healthcare, and smart homes. The number of IoT devices connected worldwide continues to rise, and 75 billion devices are expected to be connected by 2025. Even though IoT devices are rapidly spreading, they come with security and privacy challenges. Traditional methods for securing against cyber-attacks are inefficient and inadequate for securing IoT devices. This study aimed to design and implement a secure hub ecosystem prototype with an Intrusion Detection System (IDS), including Machine Learning (ML), to defend IoT devices in a smart home. After the literature about the security of IoT devices in smart homes was analyzed to identify current challenges and limitations, a secure IoT hub ecosystem prototype was implemented. Benign data and malicious data were generated in the IoT testbed. Data was collected from the IoT smart home testbed and implemented using a supervised IDS with ML. The results demonstrate the effectiveness of network segmentation using a hub in mitigating device detection, Denial-of-Service (DoS), and Man-In-The-Middle (MITM) attacks, which were successful in unsegmented networks. Additionally, supervised machine learning classifiers, such as Random Forest and J48, exhibited exceptional performance with precision, recall, and F-measure scores exceeding 97%, highlighting their potential for detecting malicious activities and classifying IoT devices accurately. These findings underscore the importance of combining network segmentation, advanced machine learning algorithms, and user education to strengthen IoT security in smart homes. The results contribute valuable insights to the development of resilient IoT security frameworks.
Online courses with virtual teams have existed for some years but became normalized with the regulations for social distancing during COVID-19. Multinational students from institutions in three countries participated in the Global Entrepreneurship online course, an opportunity-centered Ideation Hackathon developed by the In2It-Erasmus+ team. This course has been conducted once a year since 2017. The instructors noted changes in the attitudes and emotions of the students before and after the pandemic outbreak. The psychological and technological barriers that virtual teams perceived before, such as unclear communications, disappeared after the lockdowns. The feedback surveys, completed after finishing the courses in 2019 (pre-pandemic - 127 students) and 2020 (post-pandemic - 155 students), were compared quantitatively and qualitatively to find dissimilarities. The students broadly acknowledged pedagogical content and teamwork. Social relations, particularly informal interactions, played a significant role in the success of the course and its resilience during the pandemic. The students developed a strong sense of belonging to their team, enabling them to overcome the problems usually encountered in distance learning courses. The enthusiasm of students about this course after COVID-19, in which they learn and practice, opens the door to new types of studies - multicultural, international, experiential, and cooperative.
It seems that most educational systems nowadays are focused on just part of human intelligence (basically logical, mathematical, and lingual), which implies a lot of general assumptions and actions in the teaching process. As a result, many students are treated as untalented because their dominant intelligence is not the one valued in today’s schools. This causes both frustration and educational problems, not to mention the fact that many great abilities are being wasted due to being undiscovered. This article aims to investigate the possibility of using Artificial Intelligence (AI) to define individual intelligence profiles, which can be used to outline personal development paths in line with the specific set of talents of every human being. Based on the literature review, the authors have identified a need for further qualitative research aimed at projecting a roadmap focused on discovering the complete personality profile of everyone with the use of AI and then creating the optimal development path for people to reach their full potential. This will be very advantageous for both individuals and the whole society. We kindly invite all the researchers interested in implementing AI in holistic talent diagnosis and development to contribute to a common, systematic, and interdisciplinary research project that would aim at taking full advantage of AI opportunities for individuals, specific communities, and the whole society.
Software development is a domain that impacts numerous facets of the world. Careers in software development offer attractive financial compensation. However, the nature of software development jobs often requires sacrifices that may reduce job satisfaction, particularly due to ethical implications in the workplace. This paper examines how ethical perception is related to the job satisfaction of software developers. Using unique data from a 2018 survey of software developers, this paper employs ordinal logistic regression to measure the relationships between job satisfaction and various aspects of ethical perception, while controlling for age, gender, race and ethnicity, country, education level, undergraduate major, employment status, and salary. Job satisfaction among software developers is found to be positively related to ethical perception. Developers who uphold ethical standards by considering the ethical implications of the code they write, declining to write unethical code, and whistleblowing express greater job satisfaction. This study contributes to the field of disciplinary-based Knowledge Management (KM) by offering insights into the relationship between ethical perception and job satisfaction for software developers. The results can be applied in managerial decisions to improve employee engagement, retention, and productivity.
This study aimed to analyze the relationship between environmental factors, Knowledge Management (KM), absorptive capacity, KM maturity level, and innovation capacity. This research used a descriptive survey of the field and was carried out from a quantitative perspective through an online questionnaire. Then, a multivariate exploratory factorial analysis was carried out, which was followed by Structural Equation Modeling (SEM) to identify and verify significant relationships, both analyses were done using Partial Least Squares (PLS). The PLS-SEM results indicated a high level of significance in the relationship between the organizational environment and KM and innovation capacity respectively. Regarding the hypotheses posed by the research model, positive influences were found in the relationships between environmental factors and knowledge transfer, knowledge transfer and maturity level, as well as maturing level and innovation capacity. Hypotheses involving absorptive capacity were also confirmed. The research framework highlights factors that impact KM and assist in their practical application to reach a high level of knowledge maturity, thus conferring a constant strategic advantage in terms of innovation capacity. A model including organizational environment, knowledge transfer, absorptive capacity, knowledge maturity, and innovation capacity has never before been tested to the best of our knowledge. As for implications for the private sector, this study illuminates how these factors are related, influence each other, and contribute to increasing KM maturity and innovation capacity within a company.
Cybersecurity continues to be a major concern and presents significant challenges for businesses, governments, nonprofits, and individuals. Organizations are working diligently to mitigate threats to these entities while the rate of cybercrime continues to increase in sophistication and reach. Current models in cybersecurity are becoming increasingly ineffective against the velocity, agility, and persistence of cyber adversaries. This challenge presents an opportunity to consider concepts and methods in knowledge management to strategically evaluate and prioritize adversarial cyber threat activities. This research aims to explore a conceptual framework designed to provide cybersecurity professionals with new models for knowledge management to increase the effectiveness of the detection, mitigation, and attribution of these threats. This conceptual approach draws from the literature on knowledge management and cybersecurity to integrate key concepts into a new theoretical framework. This paper presents a new hybrid model for cybersecurity that integrates core concepts of the classic Nonaka and Takeuchi Knowledge Spiral and the National Institute of Standards and Technology (NIST) Cybersecurity Framework augmented with emerging artificial intelligence and machine learning technologies. The development of a hybrid cybersecurity model combined with proven knowledge management strategies for continual knowledge creation and innovation represents an integrative model designed to address emerging cybersecurity challenges.
This paper investigates the dynamic interplay between Artificial Intelligence (AI) and human logic in the domain of mathematical problem-solving. By critically examining a series of case studies, we compare the efficacy of AI-generated solutions, particularly those offered by ChatGPT, against traditional human problem-solving methods. The study employs various mathematical challenges, ranging from abstract logical puzzles to applied numerical problems, to evaluate AI's problem-solving approach and alignment with human cognitive processes. Our analysis highlights instances where AI's computational strategies complement or diverge from human reasoning, shedding light on AI's potential and limitations in deciphering mathematical problems. Furthermore, we explore the implications of integrating AI tools in educational contexts, specifically their role in enhancing students' mathematical problem-solving skills. The paper aims to contribute to the ongoing discourse on the optimal utilization of AI in education, proposing a balanced approach that leverages AI's computational power while fostering the depth and creativity of human logic. Through this comparative study, we advocate for a collaborative model where AI and human reasoning merge to enrich the educational landscape, particularly in the teaching and learning of mathematics.
This study investigates the contemporaneous effects of virtual badges on knowledge contribution in a Question and Answer (Q&A) community. Drawing on regulatory fit theory, we propose a conceptual framework of gamified reward specificity to explain how winning some types of badges can stimulate users’ contemporaneous knowledge contributions more likely than others. This study empirically assesses such contemporaneous effects by conducting logistic regression analyses on the data collected from Stack Overflow. Our findings suggest that attaining a specific badge can increase users’ contemporaneous knowledge contributions related to that badge while earning a non-specific badge can decrease such contemporaneous contributions. These findings contribute a new perspective to the existing literature and address overlooked aspects of gamification practices, offering innovative insights into designing gamified reward systems more effectively in Q&A communities.
The Information Technology (IT) Support Engagement Conceptual Model was considered in light of IT service management (ITSM) frameworks, including the IT Infrastructure Library (ITIL) and the Service Support and Service Delivery (SSSD) models in addition to the Ansoff strategic planning model. The IT service growth strategies model proposes a standardized approach to frame the enterprise Business to Business (B2B) IT services growth strategies from operational efficiency, diversification, evaluation, and incubation. The model identifies the IT support service strategies and priorities by examining the product maturity and support service status in the organization. The IT Support Conceptual Engagement Model provides a standardized decision-making strategic framework to view the support service ‘onboarding’ decision-making criteria. This model proposes three service influencing constructs that are; impact, volume, and complexity, aligned with corporate strategy to govern the service delivery decision-making process. The main contribution of this paper is to introduce the IT Support Engagement Conceptual Model as a novel framework to explain how enterprises can capture, identify, and strategize opportunities to expand an organization’s IT services in line with the corporate objectives. The model recommends different approaches and strategies to deal with different impact, volume, and complexity influencing factors while catering to any organization’s nuanced factors.
Cardiovascular Diseases (CVDs) are a leading cause of death worldwide. General at-home care has been shown to improve patient outcomes, decrease hospital admissions, and prevent fatal arrhythmias. The purpose of this research is to frame the use of at-home electrocardiograms (ECG) and the ECG readability across two groups: conducted by qualified healthcare professionals at a clinic and conducted by patients or their caregivers at-home. The results compare at-home ECG readability measured by patients and their caregivers with the control group, represented by ECG readability taken by qualified healthcare professionals during routine office visits. This research study also evaluated data for the accuracy level in ECG data using a 12-lead internal and three external leads. With the growth of modern healthcare technology, it is now possible for patients to be more proactive in monitoring their CVD by conducting at-home ECGs with real-time feedback from their cardiologist to identify any abnormalities. At-home medical-grade ECGs can lead to early identification of heart arrhythmia and decreased hospitalization frequencies. Results from this study support the need for effective coaching and training of patients and their caregivers in using at-home ECG.
The close links of Information Management (IM) and Information Technology (IT) create an evolving environment of tasks and processes. Management standards are normative descriptions of an agreed-upon set of management tasks and suggested ways of task execution. Certain standards like ITIL and COBIT (both used as a brand only by the owners, Axelos and ISACA respectively) have been popular for a long time in sub-areas of IM including IT governance, IT service management, or IT project management. Driven by digitalization, the number and update frequency of IM-related standards have significantly increased recently, making standard selection and implementation more difficult. This study presents a systematic mapping of the current state of IM standardization with respect to standardization bodies, types of standards, and certifications. Visual maps provide an overview of the IM standard landscape and reveal relevant topics and other categories. The article identifies the most relevant standardization bodies, standard types, and topics of the IM domain based on a full set of 109 IM standards. As a mapping outcome, the correlations of standardization bodies versus standard types, and of the topics versus IM task areas are clearly arranged in diagrams.
