Introduction to Generative AI in Web Engineering

Starting today and due to the advent of generative AI, traditional enterprise portfolio management (EPM) is seriously disrupted, creating new opportunities for organizations to accelerate strategic alignment, optimize resource allocation, and foster innovation. This paper explores how generative AI will have the transformative power to create actionable insights and improve decision-making in EPM. Therefore, organizations can surpass traditional portfolio management's limitations and adapt to dynamic and real-time adjustments enabled by the use of predictive analytics and scenario modeling through AI-driven tools. AI's role in enhancing portfolio optimization, risk management, and project prioritization is discussed in this article, alongside challenges centered on data quality, workforce, and ethical considerations. It further explains the integration between AI and the existing enterprise system to provide a more encompassing, flexible approach to portfolio management. The paper applies generative AI in the real world through case studies from some leading organizations, which show how generative AI has been used to optimize portfolio performance, manage risks, and allocate resources efficiently. The paper ends with the vision for the future of EPM through generative AI, where generative AI powers more agile and data-driven decision-making processes. With each growing step of AI technology, it follows that businesses need to invest money to benefit from AI tools and train their staff for the transformation of AI-enhanced portfolio management.

This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology.

Aim/Purpose: This study aims to explore the Generative AI (GenAI) literacy of English-medium instruction (EMI) undergraduates, with a particular focus on disciplinary variations across engineering, mathematics, and humanities and social sciences. Specifically, it seeks to a) examine differences in GenAI literacy across disciplines, b) identify distinct literacy profiles among disciplinary groups, and c) understand students’ self-perceptions of their GenAI literacy. Background: The advent of GenAI tools has reshaped higher education, offering personalised academic support and aiding non-native English speakers. While traditionally focused on STEM fields, the widespread adoption of GenAI, alongside concerns about accuracy and ethics, emphasises the urgent need for GenAI literacy education across all disciplines. However, gaps remain in the literature regarding how students’ GenAI literacy varies across disciplines and how their underlying literacy profiles are shaped, particularly in EMI contexts, where non-native English-speaking students face additional linguistic challenges. Methodology: Utilising a mixed-methods approach, the research assesses five critical dimensions of GenAI literacy: basic technical proficiency, communication proficiency, creative application, critical evaluation, and ethical competence. The quantitative phase, which included 347 questionnaire participants recruited via convenience sampling, employed the Kruskal-Wallis test to examine disciplinary differences in GenAI competencies. Further, a multigroup latent profile analysis was conducted to identify distinct literacy profiles. To complement the quantitative findings, follow-up semi-structured interviews were carried out with 24 students to collect in-depth qualitative data. These interviewees were drawn from the questionnaire participants using a nested sampling strategy. Reflexive thematic analysis was then applied to uncover key themes related to students’ perceived GenAI literacy. Contribution: This study highlights notable variations in GenAI literacy among students across different disciplines and identifies distinct learner profiles within an EMI university context. The findings underscore the importance of considering both disciplinary and learner-profile factors when developing educational strategies. This work offers a foundation for designing equitable and targeted strategies to develop students’ AI literacy across all disciplines, a pressing need in EMI contexts where learners navigate additional linguistic challenges. Findings: The Kruskal-Wallis test results indicated that, with the exception of ethical competence, engineering students outperformed their peers in mathematics, humanities, and social sciences across four dimensions of GenAI literacy: basic technical proficiency, communication proficiency, creative application, and critical evaluation. Additionally, the multigroup latent profile analysis identified three distinct literacy profiles across disciplines: Foundational Learners, Balanced Practitioners, and Proficient Achievers. Complementary qualitative insights from interviews corroborated these findings and provided nuanced explanations of the underlying patterns. Recommendations for Practitioners: Synthesising these insights, evidence-based pedagogical recommendations are proposed: the integration of AI literacy courses across all disciplines to foster foundational competencies and equitable access, and the implementation of profile-specific educational strategies to enhance personalised learning. Recommendation for Researchers: Refining methods for assessing GenAI literacy is recommended for future studies to enhance their validity and reliability. This includes employing more representative sampling, integrating observation-based or task-based measures alongside self-reported literacy levels, and further refining the theoretical frameworks underpinning AI literacy in response to evolving technologies. Impact on Society: The insights from this study into GenAI literacy and its disciplinary variations will enable universities to develop more responsive and inclusive educational strategies, ultimately fostering a more AI-literate society. This will ensure that graduates across all disciplines are better prepared to effectively and critically engage with AI technologies in their future careers and daily lives. Future Research: Future research should consider stratified sampling across multiple institutions, regions, and cross-national contexts to capture a more representative picture of GenAI engagement and facilitate meaningful comparisons across educational systems. In addition, while the study focused on undergraduates, postgraduate students, and academic staff are also critical stakeholders in the AI literacy agenda. Investigating how these groups engage with GenAI could provide valuable comparative insights. More importantly, the identification of learner profiles also raises new questions about movement between profiles over time and the kinds of interventions that support such transitions. Longitudinal studies and action research involving instructional design experiments could help clarify how GenAI literacy evolves and what pedagogical strategies are most effective for supporting growth.

Introduction: Generative AI (GenAI) is widely used in industry and education, offering benefits like improved writing and personalized learning, but also challenges such as unverified information, over-reliance, and academic dishonesty. This study addresses these challenges by designing guidelines for responsible use of GenAI, considering the perspective of the student. Methodology: We applied the Design Science Research Methodology. The requirements for the guidelines were collected through desk research and semi-structured interviews. This input was used in the first design iteration of the guidelines and was evaluated in a student workshop. A second and third iteration followed, based on feedback from two workshops with teachers. Results: 17 interviews, 15 workshop participants, and desk research led to 825 minutes of audio, 219 codes in 640 quotes, 10 survey responses and 28 potential guidelines. The final guidelines include do’s and don’ts, a justification and referencing approach, and ethical considerations. They aim to support responsible GenAI usage while maintaining academic integrity. Discussion: This study offers insights into communication and alternative assessment methods. Literature highlights concerns about computer science students using GenAI, such as reduced critical thinking and unverified use of information. By integrating a reflection component into the justification model, students are encouraged to stay critical. Finally, the study contributes to existing research with practical guidelines, including do’s and don’ts, and a model for GenAI justification. Conclusion: This study developed guidelines that offer a clear framework for the use of GenAI that ensures academic integrity.

The rapid development and availability of Generative AI (GenAI) tools, such as ChatGPT, offer new possibilities for academia, yet they also raise concerns for academic integrity. Chapelle's (2024) seminal work on GenAI’s implications serves as an impetus for this study, which explores teaching academic integrity in the context of AI use. Chapelle frames GenAI as an ‘inflection point’, calling for a re-evaluation of pedagogical strategies to support students in understanding its technological and ethical implications. This article presents findings from a mixed-methods study at a university in Cyprus, where ESL undergraduates in academic writing classes engaged with ChatGPT. Participants with B2/C1 English proficiency from diverse cultural backgrounds were assessed through pre- and post-intervention evaluations and qualitative interviews. A semester-long intervention incorporated activities to enhance academic writing skills and comprehension of ethical AI use, focusing on critical thinking, proper citation, and policy adherence. Findings indicate that students demonstrated increased AI knowledge and awareness of academic integrity. This study contributes to the discourse on integrating GenAI in academia, providing insights into effective instructional practices and addressing ethical considerations in academic writing. It offers guidance for educators and institutions aiming to integrate GenAI responsibly, ensuring academic integrity remains central in second language education.

This study explores the self-directed use of Generative AI (GenAI) in academic writing among advanced L2/multi-lingual English writers, challenging the assumption that GenAI undermines meaningful learning. Through case studies, we investigate how three (post)doctoral writers engage with GenAI to address specific L2 writing challenges. The findings revealed a spectrum of approaches to GenAI, ranging from prescriptive to dialogic uses, with participants positioning AI as a tool versus an interactive participant in their meaning-making process, reflecting different views of AI as a mechanical system, social construct, or distributed agency. We highlight the ways AI disrupts traditional notions of authorship, text, and learning, showing how a post-structuralist lens allows us to transcend human-AI, writing-technology, and learning-bypassing binaries in our existing discourses on AI. This shifting view allows us to deconstruct and reconstruct AI’s multi-faceted possibilities in writers’ literacy practices. We also call for more nuanced ethical considerations to avoid stigmatizing multi-lingual writers’ use of GenAI and to foster writerly virtues that reposition our relationship with AI technology.

Exploring generative AI's role in alleviating nursing workload and burnout in Taiwan

Generative AI has emerged as a transformative technology capable of reshaping public sector operations by streamlining administrative tasks, enhancing data analytics, and driving more personalized citizen interactions. Despite its potential, a critical gap remains in understanding how generative AI can be effectively integrated into government processes, particularly in developing countries like Indonesia, where bureaucratic inefficiencies and infrastructural constraints persist. This study addresses this gap by conducting a mixed-method investigation that combines a Systematic Literature Review (SLR) of generative AI applications across multiple sectors to inform public service innovation, with expert interviews from key stakeholders Indonesian government, academic, and industry. The findings indicate that generative AI holds substantial promise for reducing manual processes, improving inter-agency coordination, and fostering data-driven decision-making. However, challenges related to ethical considerations, data privacy, and limited digital infrastructure present significant barriers to widespread adoption. By proposing a governance framework and offering strategic recommendations tailored to the Indonesian context, this research provides actionable insights for policymakers, government agencies, and technology providers. In doing so, it contributes to the evolving discourse on AI-driven public service innovation and offers a pathway toward realizing a more efficient and citizen-focused smart government in Indonesia.

This study examines the educational effects of a generative AI music creation workshop conducted as part of STEAM education that integrates the arts (in this case, music creation) into science, technology, engineering, and mathematics (STEM). The workshop, held at Osaka Metropolitan University College of Technology, employed state-of-the-art generative AI tools such as ChatGPT and Suno, a music generation AI. It provided students with the opportunity to write their lyrics and collaborate with AI to compose music. Additionally, AI literacy education was incorporated to deepen understanding of the risks and ethical challenges associated with AI, aiming to balance creative activities with the responsible use of technology. Results from a post-workshop survey indicated that students experienced heightened creativity and self-expression through collaboration with generative AI, and many reported feeling a sense of excitement and anticipation (“wakuwaku”) during the process. Notably, creating music with AI was found to enhance students’ creative self-efficacy and promote sustained motivation for long-term AI use. However, some participants also expressed concerns about over-reliance on AI and reported psychological barriers, highlighting areas for improvement in future implementations. This study not only demonstrates that STEAM education incorporating generative AI is an effective approach to fostering students’ creativity and motivation to learn, but also addresses practical challenges such as ethical considerations and the need for supportive educational environments. Furthermore, as a successful case supported by The Asia Foundation and Microsoft, the program suggests the potential for collaboration among industry, academia, and government in advancing STEAM education.

Generative AI (Gen-AI) is transforming industries, and the automotive sector is no exception. Auto Component Manufacturing (AutoCM) organizations face strong pressure to adopt Gen-AI for competitiveness, yet little research has examined adoption factors in this context. This study investigates the determinants of Gen-AI adoption in AutoCM firms using the Technology-Organization-Environment framework. A model was developed and tested through survey data from 490 managers, validated via PLS-SEM. The findings reveal key predictors of adoption: perceived intelligence, data quality, absorptive capacity, IT infrastructure, top management commitment, vendor support, competitive pressure, and government support. Results further show that adoption intention drives potential use of Gen-AI, but this relationship is weakened by concerns over Gen-AI hallucination. This research identifies crucial drivers of Gen-AI adoption in AutoCM, extends TOE framework literature, and provides actionable insights for practitioners to strategically harness Gen-AI’s transformative potential in the sector.

Generative AI is revolutionizing nursing by enhancing clinical decision-making, patient care, and workflow efficiency. By integrating predictive analytics, personalized care planning, and real-time decision support, AI enables nurses to deliver proactive, evidence-based, and patient-centered care. Applications include automated documentation, intelligent staffing, remote monitoring, and culturally sensitive patient communication, improving adherence, satisfaction, and safety. Despite its promise, challenges such as data quality, bias, and ethical considerations necessitate careful implementation and workforce training. The future of smart nursing lies in a collaborative approach where human expertise and AI innovation work synergistically to optimize healthcare delivery.

The integration of Generative AI (GenAI) in Education presents immense potential for reshaping learning experiences and empowering students and educators. However, harnessing this potential requires collective action and responsible decision- making to ensure the effective and ethical use of AI technologies. This paper presents a series of recommendations and proposals aimed at effectively integrating GenAI in the higher education sector, catering to the perspectives of government, AI developers, students, educators, universities, schools, and researchers. By exploring diverse viewpoints about ChatGPT and future Google Gemini, this research aims to create a comprehensive recommendation guiding regulatory measures that address challenges, ethical considerations, and best practices of GenAI integration. Through a holistic approach, researchers believe that policymakers can foster a transformative and ethical environment, leveraging the full potential of generative AI while safeguarding students' well-being and academic integrity.

This paper examines the transformative potential of Generative AI (GenAI) in enhancing data collection and analysis methodologies within educational research. By utilizing automated techniques such as web scraping and sentiment analysis, researchers can efficiently gather substantial amounts of data while minimizing human error and bias, thereby improving the validity of their findings. Furthermore, advanced analytical approaches, including predictive analytics and natural language processing, enable deeper insights into complex educational phenomena that traditional methods may overlook. The paper underscores the ethical considerations associated with GenAI, particularly concerning data privacy, informed consent, and bias mitigation. It advocates for ongoing innovation in research methodologies to adapt to these technological advancements and highlights the importance of interdisciplinary collaboration in refining research practices. By responsibly integrating GenAI, researchers can enhance educational outcomes and promote equitable learning experiences. Ultimately, this work encourages scholars to reflect on the future integration of GenAI in their research practices and to consider how these innovations can reshape the educational landscape.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

Generative AI in e-commerce enhances demand forecasting and pricing strategies, improving operational efficiency and increasing customer satisfaction. This paper comprehensively overviews how generative AI adds value to intelligent demand forecasting and adaptive pricing models. It enables online businesses to predict consumer behavior and adjust prices dynamically. By analyzing key applications, this study highlights the advantages of AI-powered algorithms, including improvements in inventory management, adaptation to market fluctuations, and large-scale customer personalization. It also addresses ethical considerations, such as data privacy and transparency in AI-driven pricing, offering a well-rounded perspective on implementation This work supports the argument that generative AI provides a competitive edge, with extensive research findings from real-world applications helping to establish this.