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Generative Artificial Intelligence (GenAI) has the potential to contribute to the reconfiguration of processes and to increase the level of innovation in the business environment. However, current research highlights that GenAI-based commercial applications are still in a ‘ferment phase’, with their impact not yet fully understood. To address this gap, the present study aims to examine the main activities and industries in which GenAI can contribute to enhancing innovation. To achieve this objective, a mixed methodological approach was adopted, starting with a series of quantitative analyses based on data extraction through automated techniques, followed by a content analysis designed to create a framework of innovation opportunities in the business environment. The dataset used for this study consists of 96 patents collected from the Espacenet registry, published between 2023 and 2025 under the G06Q classification. The research findings highlight an extensive framework of GenAI applications, organised into six categories, which can be successfully employed to increase business innovation. A predominant focus of the inventions on personalisation and automation solutions can be observed, but emerging trends can also be noted in industries such as education, healthcare, financial and banking services, and energy. Thus, this study reduces the gap identified by scholars, laying the foundation for future research focused on business innovation through GenAI-driven solutions.

This paper systematically reviews the impact of Generative Artificial Intelligence (GenAI) in education from 2021 to 2024. The objective is to explore key trends, geographical distribution of research, and emerging themes in the educational use of GenAI, while addressing ethical challenges such as algorithmic bias, data privacy, and the digital divide. Using a systematic review methodology guided by four research questions, the study analyzes publications in Scopus to identify dominant research themes and leading countries in the field. Results indicate that the United States, the United Kingdom, and Singapore are the top contributors to GenAI research, with a primary focus on personalized learning and automated assessments. The review highlights a surge in publications, particularly in 2023, driven by advancements in AI tools like ChatGPT. It emphasizes the importance of international collaboration and proposes the need for regulatory frameworks to ensure the ethical integration of AI in education. This review offers valuable insights into the current state of GenAI research in education and provides recommendations for educators, policymakers, and researchers to navigate the challenges and opportunities of AI-driven learning.

Biosensors are crucial to the diagnosis process since they are designed to detect a specific biological analyte by changing from a biological entity into electrical signals that can be processed for further inspection and analysis. The method provides stability while evaluating cancer cell imaging and real-time angiogenesis monitoring, together with a robust, accurate, and successful identification. Nevertheless, there are several advantages to using nanomaterials in biological therapies like cancer therapy. In support of this strategy, gamification creates a new framework for therapeutic training that provides patients and first aid responders with immunological, photothermal, photodynamic, and chemo-like therapy. Multimedia systems, gamification, and generative artificial intelligence enable us to set up virtual training sessions. In these sessions, game-based training is being developed to help with skin cancer early detection and treatment. The study offers a new, cost-effective solution called GAI, which combines gamification and general awareness training in a virtual environment, to give employees and patients a hierarchy of first aid instruction. The goal of GAI is to evaluate a patient’s performance at each stage. Nonetheless, the following is how the scaling conditions are defined: learners can be divided into three categories: passive, moderate, and active. Through the use of simulations, we argue that the proposed work’s outcome is unique in that it provides learners with therapeutic training that is reliable, effective, efficient, and deliverable. The examination shows good changes in training feasibility, up to 22%, with chemo-like therapy being offered as learning opportunities.

The rapid development of generative artificial intelligence (GenAI) has generated significant economic and social value, alongside risks to user privacy. For this purpose, this study investigates privacy protection in human-AI interaction by employing a combined approach of evolutionary game and system dynamics. A three-party game model was developed to analyze the interactive effects and evolution of privacy protection strategies among the government, GenAI company, and users. Sensitivity analysis through system dynamics simulations was conducted on four kinds of factors—government, company, users, and incentive mechanisms, to reveal how these factors influence the strategy choices of the three parties. The results suggest that the government’s reputation, subsidies, free-riding benefits, fines, rewards from GenAI company to users, and the cost–benefit considerations of all three parties are key factors affecting strategic decisions. Moderate fine and subsidy policies can effectively promote privacy protection, with subsidy policies proving to be more effective than penalty policies. This paper provides theoretical support and decision-making guidance for balancing technological development and privacy protection in human–AI interaction, contributing to the regulated and orderly development of Generative Artificial Intelligence.

Generative Artificial Intelligence (GenAI) tools hold significant promises for enhancing teaching and learning outcomes in higher education. However, continues usage behavior and satisfaction of educators with GenAI systems are still less explored. Therefore, this study aims to identify factors influencing academic staff satisfaction and continuous GenAI usage in higher education, employing a survey method and analyzing data using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation Confirmation Model (ECM) as its theoretical foundations, while also integrating ethical concerns as a significant factor. Data was collected from a sample of 127 university academic staff through an online survey questionnaire. The study found a positive correlation between effort expectancy, ethical consideration, expectation confirmation, and academic staff satisfaction. However, performance expectancy did not show a positive correlation with satisfaction. Performance expectancy was positively related to the intention to use GenAI tools, while academic staff satisfaction positively influenced the intention to use GenAI. The social influence did not correlate positively with the use of GenAI. Security and privacy were positively associated with staff satisfaction. Facilitation conditions also positively influenced the intention to use GenAI. The findings of this study provide valuable insights for academia and policymakers, guiding the responsible integration of GenAI tools in education while emphasizing factors for policy considerations and developers of GenAI tools.

In modern cybersecurity scenario, an intrusion detection system (IDS) should not only be highly predictive but also be dynamically responsive to new attack vectors. In this paper, the hybrid architecture is proposed, and it combines graph-based, sequential, and tabular learning into one architecture, which is backed by the Generative AI-based cycle of data augmentation. Such a design can which enhances the F1-score by 3.7 percent and reduces the false-positive rate by 38 percent of the baseline deep and ensemble IDS models, such as CNN-LSTM, AE-XGBoost, and GAT-IDS. With adversarial perturbations implemented on both FGSM and PGD, the loss in detection performance is less than 5% reflecting a large adversarial robustness. The generative augmentation and unified embedding fusion are the key features that differentiate the suggested design compared to the previous hybrid IDS design, providing a scalable and reproducible way to guard against cyber-threats adaptively.

The study aims to identify the knowledge, skills and competencies required by accounting and auditing (AA) professionals in the context of integrating disruptive Generative Artificial Intelligence (GenAI) technologies and to develop a framework for integrating GenAI capabilities into organisational systems, harnessing its potential to revolutionise lifelong learning and skills development and to assist day-to-day operations and decision-making. Through a systematic literature review, 103 papers were analysed, to outline, in the current business ecosystem, the competencies’ demand generated by AI adoption and, in particular, GenAI and its associated risks, thus contributing to the body of knowledge in underexplored research areas. Positioned at the confluence of accounting, auditing and GenAI, the paper introduces a meaningful overview of knowledge in the areas of effective data analysis, interpretation of findings, risk awareness and risk management. It emphasizes and reshapes the role of required skills for accounting and auditing professionals in discovering the true potential of GenAI and adopting it accordingly. The study introduces a new LLM-based system model that can enhance its GenAI capabilities through collaboration with similar systems and provides an explanatory scenario to illustrate its applicability in the accounting and audit area.

This concept-centric review identifies and synthesizes emerging drivers of Generative AI (GenAI) adoption in education, addressing a critical gap by offering the first structured integration of empirically supported predictors. Based on 27 peer-reviewed studies featuring validated research models, the review distils 11 predictors into a Three-Tier Framework. Core predictors—Performance Expectancy and Trust—consistently influence adoption across contexts. Moderate predictors—Effort Expectancy, Facilitating Conditions, Social Influence, Perceived Behavioral Control, and Perceived Compatibility—show variable relevance depending on technological and institutional factors. Emerging predictors—Habit, AI Literacy, Anxiety, and Playfulness—capture evolving socio-technical and individual dynamics, reflecting the rapid development of GenAI technologies. While the current literature offers valuable insights, gaps remain in addressing ethical concerns, barriers to adoption, teacher professional development, student engagement, and the influence of cultural and contextual diversity. The findings emphasize the need to iteratively refine the Three-Tier Framework by incorporating these dimensions and adapting to technological advancements. By consolidating empirical evidence and distinguishing between mature and emerging predictors, this review advances theoretical understanding of technology acceptance in education. It provides a structured foundation for guiding future research, informing policy and practice, and supporting responsible, context-sensitive GenAI integration across diverse educational settings.

In this paper, the performance of dual-hop relay transmission in modern wireless communication systems is analyzed by considering two fundamental relaying techniques, namely, Amplify-and-Forward (AF) and Decode-and-Forward (DF). The propagation conditions on the source–relay (S-R) and relay–destination (R-D) links are modeled using the κ-μ statistical distribution, which effectively captures the fading characteristics in both line-of-sight (LoS) and non-line-of-sight (NLoS) environments. The analysis focuses on key performance metrics, including the outage probability (Pout) and average bit error probability (Pe), for Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) modulation schemes, assuming transmission via a single relay without a direct S–D link. Closed-form expressions for the considered metrics are derived based on the κ-μ model and verified by numerical evaluation. In addition to classical analytical modeling, a Generative Artificial Intelligence (GenAI)-enabled workflow is incorporated as a supportive tool in order to aid in automated analysis, the interpretation of the results in the context of network management under varying channel and system parameters based on the Pout and Pe calculations with the aim to tackle the underlying complexity and cognitive load of infrastructure adaptation and re-configuration operations. The combined analytical and GenAI-assisted approach provides valuable insights for the optimization, design, and continuous evolution of robust relay-based architectures in next-generation wireless networks.

Language teachers mostly spend much time scoring students' writing and may sometimes hesitate to provide reliable scores since essay scoring is time-consuming. In this regard, AI-based Automated Essay Scoring (AES) systems have been used and Generative AI (GenAI) has recently appeared with its potential in scoring essays. Therefore, this study aims to focus on the differences and relationships between human raters (HR) and GenAI scores for the essays produced by English as a Foreign Language (EFL) learners. The data consisted of 210 essays produced by 35 undergraduate students. Two HR and GenAI evaluated the essays using an analytical rubric divided into the following five factors: (1) ideas, (2) organization and coherence, (3) support, (4) style, and (5) mechanics. This study found that there were significant differences between the scores given by HR and those generated by GenAI, as well as variations among the HR themselves; nonetheless, GenAI's scores were similar across dual evaluations. It was also noted that GenAI's scores were statistically significantly lower than those of HR. On the other side, it was found that HR scores correlated weakly, while GenAI scores correlated strongly. A significant correlation was observed between HR-1 and GenAI across all factors, whereas the second HR-2 showed significant correlations with GenAI in only three factors. Therefore, this study can guide EFL teachers on how to reduce their workload in writing assessments by giving GenAI more responsibility in scoring essays. The study also offers many suggestions for future studies on AES based on the findings and limitations of the study.