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Learning design for impact: a transformative education model for sustainability
No full textPurpose: The purpose of this paper is to propose a model for teaching the principles of sustainability to undergraduate students that focuses on learner engagement and deep learning to significantly enhance sustainability literacy. Design/methodology/approach: The paper draws upon a case study of the second-year course “Principles of Sustainability” using the UN PRME Impactful Five pedagogical framework. The study uses document analysis, content analysis, classroom observation and artefact analysis surveys to evaluate student perceptions and student-generated content for engagement, relevance and application of sustainability principles. Findings: This paper finds that the UN PRME Impactful Five (i5) pedagogical framework can be leveraged to have a good effect, particularly within the assessment regime of the course. Preliminary feedback indicates significant student engagement, deep comprehension of sustainability issues and high student confidence in their ability to contribute to sustainable development. Research limitations/implications: The study is based on data from a course taught for only two semesters, which may not fully capture the long-term effects on student success and employability. Future research should extend the study’s duration to assess long-term effects in sustainability-related fields better. Practical implications: The study presents practical methods for educators to implement the Impactful Five Framework in education, aiming to enhance sustainability thinking and practices among students. Social implications: The paper effectively highlights essential strategies for integrating sustainability principles into the classroom, ensuring their relevance for students across all areas of life. Furthermore, the study demonstrates the broad applicability of the i5 Framework in diverse educational and social contexts. Originality/value: The i5 Five pedagogical framework is a new and innovative approach to undergraduate business and management education. Combined with an interdisciplinary curriculum, the model proposed in this paper represents a novel approach to expanding sustainability literacy across all degree programs
Radio Map Estimation Based on Generative Artificial Intelligence: Evolution from Point-Level to Cell-Level Prediction
No full textThis article explores the application of generative artificial intelligence (AI) in refining radio map estimation for complex urban wireless networks, shifting from conventional point-level to advanced cell-level predictions. Addressing the challenges posed by urban density and diversity in wireless propagation models, the study employs deep learning techniques to surmount these barriers. We propose a three-step deployment framework for generative AI, encompassing scene analysis, adaptation to new environments, and efficient network establishment. This framework empha-sizes the synergy of human expertise and AI in optimizing base station placement and network design, particularly in urban contexts. The article underscores the significance of integrating deep learning with traditional wireless communication knowledge, aiming to enhance the precision and efficiency of network configurations. By offering innovative solutions and methodolo-gies, this research contributes to the evolution of 5G-Advanced and 6G networks, highlighting the transformative role of generative AI in advancing future wireless communication technologies
Artificial intelligence in modern human resources practice
No full textThe integration of artificial intelligence (AI) in human resource management (HRM) is transforming the way organizations manage their workforce. This chapter explores the current landscape of AI in HRM, its theoretical and practical implications, and future research directions. AI-powered tools are being leveraged to automate and optimize various HR processes, from recruitment and talent acquisition to performance management and employee engagement. The chapter examines the impact of AI on HRM through the lenses of the resource-based view, social exchange theory, behavioral theories of motivation, and ethical frameworks in AI. The chapter emphasizes the importance of responsible AI implementation, considering factors such as transparency, fairness, and accountability. Future research should focus on developing explainable AI systems, investigating AI\u27s long-term impact, and designing AI-powered tools that support employee well-being
Integrating digital and narrative medicine in modern healthcare: a systematic review
Full text linkThe increasing integration of digital technologies in healthcare, such as electronic health records, telemedicine, and diagnostic algorithms, improved efficiency but raised concerns about the depersonalization of care. Narrative medicine has emerged as a pedagogical and clinical response to this shift, emphasizing the value of patient stories, socio-cultural contexts, and reflective practice. Understanding how digital tools support, rather than undermine, narrative competencies is critical for developing a more human-centered healthcare education. This review systematically examined empirical and theoretical studies on the integration of digital technologies and narrative medicine within healthcare education. Databases including PubMed, MEDLINE, and Google Scholar were searched using defined inclusion and exclusion criteria. Studies were screened, reviewed, and thematically analyzed to identify patterns in outcomes, pedagogical applications, and integration strategies. The findings indicate that narrative medicine nurtures empathy, communication, and professional identity formation among healthcare trainees. Digital tools, such as virtual reality simulations, mobile health applications, and e-portfolios, reinforce these outcomes by providing immersive, interactive, and reflective learning experiences. The integration of narrative methods into digital platforms and curricular models provides a promising approach for linking clinical competence with relational care. The convergence of digital and narrative medicine provides a compelling pedagogical framework for healthcare education. This integrated approach supports technological proficiency and humanistic values, for advancements in digital health to improve rather than displace the interpersonal foundations of patient care. Further empirical research might assess long-term outcomes and guide implementation into curricula, faculty development, and institutional policy. Background: The increasing integration of digital technologies in healthcare, such as electronic health records, telemedicine, and diagnostic algorithms, has improved efficiency but raised concerns about the depersonalization of care. Narrative medicine has emerged as a pedagogical and clinical response to this shift, emphasizing the value of patient stories, socio-cultural contexts, and reflective practice. Objective: To understand how digital tools can support, rather than undermine, narrative competencies and contribute to a more human-centered healthcare education. Methods: This review systematically examined empirical and theoretical studies on the integration of digital technologies and narrative medicine within healthcare education. Databases including PubMed, MEDLINE, and Google Scholar were searched using defined inclusion and exclusion criteria. Identified studies were screened, reviewed, and thematically analyzed to extract patterns in outcomes, pedagogical applications, and integration strategies. Results: The findings indicate that narrative medicine nurtures empathy, communication, and professional identity formation among healthcare trainees. Digital tools, including virtual reality simulations, mobile health applications, and e-portfolios, reinforce these outcomes by providing immersive, interactive, and reflective learning experiences. Integrating narrative methods into digital platforms and curricular models presents a promising approach for linking clinical competence with relational care. Conclusion: The convergence of digital and narrative medicine offers a compelling pedagogical framework for healthcare education. This integrated approach supports both technological proficiency and humanistic values, enabling advancements in digital health to enhance rather than displace the interpersonal foundations of patient care. Further empirical research is needed to assess long-term outcomes and guide implementation in curricula, faculty development, and institutional policy
Bias and its impact on hiring and promotion
No full textThis chapter examines the multifaceted nature of workplace discrimination and its impact on organizational dynamics. It investigates the psychological and social foundations of bias and the organizational and systemic factors that perpetuate discriminatory practices. The research presents evidence- based strategies for mitigating bias, such as structured interview techniques, blind resume screening, and the implementation of diverse candidate slates. The role of technology, particularly artificial intelligence, in both perpetuating and combating bias is critically analyzed. This study provides a holistic view of the challenges and opportunities in creating more equitable and inclusive workplaces, offering valuable insights for HR professionals, organizational leaders, and scholars in management and organizational behaviour
Transforming Corporate Social Responsibility and Business Ethics With AI
No full textAI technologies enable businesses to analyze data, track environmental and social outcomes, and optimize processes to ensure alignment with ethical standards and corporate social responsibility (CSR) goals. Through AI-driven tools, companies can improve transparency, enhance accountability, and identify areas for improvement in their supply chains, labor practices, and environmental impact. It can help organizations predict and mitigate risks related to social responsibility, ensuring well-informed decisions. Integrating AI into CSR and business ethics strategies may position businesses to contribute to societal well-being while maintaining long-term profitability and reputation. Transforming Corporate Social Responsibility and Business Ethics With AI explores the impact of AI across various academic disciplines, highlighting the transformative potential of AI and the critical need for cross-disciplinary collaboration. It provides insights into the current state of AI integration, future possibilities, and ethical considerations. This book covers topics such as ethics and law, management science, and smart cities, and is a useful resource for business owners, government officials, policymakers, sociologists, academicians, and researchers
Social Media Threat Intelligence: A Framework for Collecting and Categorizing Threat-Related Data
No full textThe exponential growth of social media, now encompassing approximately five billion users globally, has transformed these platforms into critical sources of information, mirroring diverse societal interactions. However, this vast data repository also introduces significant threats, including terrorism, online fraud, and the spread of disinformation, underscoring the need for robust monitoring and categorization mechanisms. This study presents an innovative framework designed to systematically collect and categorize social media data using weighted keywords tailored to various threat categories. By leveraging semi-automated data collection and keyword weighting techniques, this framework enhances threat detection accuracy and integrates diverse data collection methods such as APIs, bots, and scraping tools. Preliminary results demonstrate the framework’s efficacy in identifying and categorizing threat-related content, highlighting its potential to significantly advance threat intelligence capabilities. This groundbreaking approach promises to revolutionize social media threat intelligence, equipping organizations with the tools to anticipate emerging threats and bolster national security
Leveraging industry 4.0 and circular open innovation for digital sustainability: The role of circular ambidexterity
Full text linkThe transition to a circular economy requires a fundamental shift from traditional linear production models to resource efficiency and closed-loop systems. Industry 4.0, with its cutting-edge technologies, has the potential to accelerate digital transformation. However, its impact on digital sustainability is neither automatic nor guaranteed. Many organizations continue to adopt Industry 4.0 technologies to boost their operational efficiency and reduce costs, often failing to align them with circular economy strategies. This study, therefore, delves into the conditional role of circular open innovation in ensuring that digital transformation not only contributes to productivity but also actively fosters circular innovation and digital sustainability. To explore this, the study adopts a conceptual methodology and develops a theoretical framework grounded in a synthesis of recent scholarly literature on Industry 4.0, circular ambidexterity, and circular open Innovation. The literature-based findings suggest that circular open innovation plays a critical enabling role by facilitating cross-sector collaboration, knowledge-sharing, and co-creation, allowing organizations to fully capitalize on Industry 4.0 for circular transitions and sustainable transformation. The study also found that organizations with high circular open innovation engagement integrate open innovation platforms, blockchain-enabled supply chain transparency, and AI-driven circular analytics, leading to scalable circular business models. In contrast, those that fail to engage in circular open innovation may use Industry 4.0 solely for efficiency gains, missing opportunities to create truly regenerative systems. This study highlights that integrating Industry 4.0 with circular open innovation is crucial to achieving circular transformations and ensuring digital technologies contribute meaningfully to sustainable outcomes
An Explainable AI and Optimized Multi-Branch Convolutional Neural Network Model for Eye Anemia Diagnosis
Full text linkThis paper proposes a novel, non-invasive approach to diagnosing eye anemia using deep learning techniques. Traditional methods, reliant on invasive procedures like venipuncture, are costly and can cause patient discomfort. Our model leverages a multi-branch convolutional neural network (CNN) architecture, incorporating the Hippopotamus Optimization (HO) algorithm and multiclass support vector machines (SVMs) for enhanced accuracy. To address data imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and data augmentation. The model is trained and evaluated on a dataset of 211 eye images. The model achieves a remarkable 97.06% accuracy, with a Receiver Operating Characteristic (ROC) curve demonstrating an Area Under the Curve (AUC) of 0.973, indicating strong discriminative power. The parallel branch CNN architecture significantly improves training speed and reduces inference time. Furthermore, t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization effectively clusters data points, showcasing the model\u27s ability to distinguish between anemic and non-anemic cases. To ensure model transparency and reliability, we utilize the SHapley Additive exPlanations (SHAP) method to understand feature importance. This non-invasive approach holds significant promise for early and efficient anemia detection, particularly in resource-constrained settings
Enhancing teachers’ design thinking mindsets through reflective practice: Cultivating innovation in an upskilling STEAM training program
Full text linkPromoting innovation in education through enhanced design thinking (DT) mindsets among teachers is a central goal. This study examines the use of reflective practice within an upskilling STEAM training program to achieve this enhancement. The upskilling training initiative examined in this research encompasses a ten-week instructional session characterized by a hybrid learning methodology under the guidance of an instructor. An explanatory mixed method design is employed, starting with a quantitative method (teachers’ survey) followed by a qualitative method (focus group discussion). The participants were in-service K-12 teachers (n = 55) from charity schools across Northern Emirates in the United Arab Emirates. The main results demonstrate that reflective practice significantly contributed to developing teachers’ DT mindsets, fostering empathy, creative confidence, and the ability to navigate uncertainty. Teachers reported increased confidence in guiding students through open-ended problem-solving tasks and adapting their instructional strategies to address feedback from students and peers. This research highlights reflective practice\u27s transformative potential in elevating teachers’ DT mindsets. For example, through iterative reflection, teachers were better equipped to design STEAM challenges that connect with real-world problems, allowing students to apply creativity and critical thinking. It demonstrates the link between the upskilling STEAM program and the cultivation of innovative teaching methods, equipping educators to nurture creativity and problem-solving skills in students, thereby enriching education