Advances In Artificial Intelligence Research Articles

Enhancing General Aviation Safety: The Integration of Generative AI in Preflight Weather Planning

Artificial Intelligence (AI) permeates daily life and industries through enhanced machine learning, natural language processing, and computer vision. Generative AI, a significant advancement, mimics human-like text and makes data-driven decisions, particularly in generative AI applications. Exploring novel AI applications is crucial, such as using generative AI to aid General Aviation (GA) pilots in preflight weather planning, with the aim to enhance pilots’ awareness and reduce weather-related accidents. However, the rapid evolution of generative AI raises many concerns like volatility, security risks, and decision-making biases. Preflight weather planning is vital in aviation, with weather-related incidents comprising a significant portion of accidents. Despite advancements, GA pilots’ interpretation of weather information remains subpar. This paper examines how generative AI may have potential to assist GA pilots to perform preflight weather planning, while also addressing risks and suggesting ethical research directions.

AI-driven Business Model Innovation - Where Technology Meets Strategy

Business Model Innovation (BMI) involves redefining how organizations create, deliver, and capture value. With the advent of Artificial Intelligence (AI), businesses are increasingly leveraging these technologies to transform their models. The current research investigates the impact of AI on BMI using primary data collected from various industries. It employs statistical analyses to understand the extent of AI adoption, its effects on business model components and various key business metrics. The study identifies key trends in AI adoption across functions and industries. Key findings reveal significant correlations between AI usage and improvements in key business metrics such as operational efficiency, customer engagement, and revenue streams thus resulting in an organization’s competitive advantage. Businesses can make better-informed decisions, establish more effective workflows, and produce more powerful marketing campaigns as a result of AI adoption. It also provides a competitive edge and lays the framework for future growth. This academic work makes a substantial contribution to the discourse on AI for business model innovation, where technology is leveraged to optimize strategy.

Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools

The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these techniques across multiple medical imaging datasets, including a DCE-MRI dataset for breast-tumor image segmentation and classification (BreastDM) and the Breast Ultrasound Image (BUSI), Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Image (BreakHis), and Digital Database for Screening Mammography (DDSM) datasets. The integration of advanced AI models, such as the vision transformer (ViT) and the U-KAN model—a U-Net structure combined with Kolmogorov–Arnold Networks (KANs)—is another key aspect, offering new insights into the efficacy of these approaches in different imaging contexts. Experiments revealed that Wiener filtering significantly improved image quality, achieving a peak signal-to-noise ratio (PSNR) of 23.06 dB and a structural similarity index measure (SSIM) of 0.79 using the BreastDM dataset and a PSNR of 20.09 dB with an SSIM of 0.35 using the BUSI dataset. When combined filtering techniques were applied, the results varied, with the MIAS dataset showing a decrease in SSIM and an increase in the mean squared error (MSE), while the BUSI dataset exhibited enhanced perceptual quality and structural preservation. The vision transformer (ViT) framework excelled in processing complex image data, particularly with the BreastDM and BUSI datasets. Notably, the Wiener filter using the BreastDM dataset resulted in an accuracy of 96.9% and a recall of 96.7%, while the combined filtering approach further enhanced these metrics to 99.3% accuracy and 98.3% recall. In the BUSI dataset, the Wiener filter achieved an accuracy of 98.0% and a specificity of 98.5%. Additionally, the U-KAN model demonstrated superior performance in breast cancer lesion segmentation, outperforming traditional models like U-Net and U-Net++ across datasets, with an accuracy of 93.3% and a sensitivity of 97.4% in the BUSI dataset. These findings highlight the importance of dataset-specific preprocessing techniques and the potential of advanced AI models like ViT and U-KAN to significantly improve the accuracy of early breast cancer diagnostics.

A Review of the Latest Research Achievements in the Basic Theory of Generative AI and Artificial General Intelligence (AGI)

A Review of the Latest Research Achievements in the Basic Theory of Generative AI and Artificial General Intelligence (AGI)

Constructing a Socio-Legal Framework Proposal for Governing Large Language Model Usage and Application in Education

Due to the fast-changing environments caused by artificial intelligence development, the socio-technical challenge in contemporary educational systems focuses on the need for more regulative measures guiding system stakeholders’ behavior. In fulfilling the present legal gap, enacted soft law regulation has been laid out, and a detailed systematic literature review was conducted in the paper presented. The specific methodological approach was selected to deal with two crucial research tasks: to reveal and recommend fundamental governing mechanisms regarding the use and application of generative artificial intelligence; more precisely, large language models in educational systems. Three systematically guided layers of quantitative and qualitative content analysis of central policy, legislation, and regulatory mechanisms in governing AI in education were extracted from the 142 Scopus Database and Web of Science research papers analyzed and presented. These research findings benefit policymakers, regulatory and legislative bodies, and agencies in constructing governing frames for using and applying generative artificial intelligence in education.

Transforming Data Analytics with AI for Informed Decision-Making

This study delves into how advanced data analytics and artificial intelligence (AI) can work together to enhance decision-making processes. As we navigate today’s data-driven environment, discovering the synergy between these fields is crucial, given the growing complexity of datasets. Advanced analytical tools are essential, and AI offers exceptional capabilities in pattern recognition and automation. This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making. A key focus of the study is on making AI models more interpretable and transparent. It emphasizes the importance of ensuring that AI-driven decisions are clear and understandable. Additionally, the research addresses ethical considerations and the need for human-centered design, aiming to balance AI’s power with openness. It also strives for responsible AI use by tackling issues such as bias and promoting ethical practices in the application of advanced data analytics and AI. The study demonstrates practical applications in areas like healthcare and finance, showing how these technologies can transform personalized medicine, disease prediction, risk assessment, fraud detection, and market trend analysis. Overall, this research highlights the valuable interaction between advanced data analytics and AI, offering a guide for organizations to enhance their decision-making while adhering to ethical standards and responsible AI use.

Place identity: a generative AI’s perspective

Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies.

The MADE Framework: Best Practices for Creating Effective Experimental Stimuli Using Generative AI

This paper introduces the MADE (Mapping, Assembling, Demonstrating, Executing) framework, a comprehensive set of best practices for the ethical and effective use of generative artificial intelligence (AI) in creating experimental stimuli for advertising research. The framework was developed through an extensive exploration of various emergent generative AI tools used in common experimental manipulations. We apply the MADE framework to demonstrate the creation of high-quality, realistic experimental ads using leading generative AI tools. Our empirical testing shows that AI-generated stimuli are valid, with consumers rating them equally high in quality, appropriateness, and realism compared with professionally created ads. This finding underscores the viability of AI-generated ads in advertising research. Additionally, we discuss the importance of adhering to ethical standards and ensuring transparency in AI use. By combining technological innovation with methodological rigor, this paper aims to guide researchers in leveraging the potential of generative AI while addressing its ethical implications, thereby enhancing the realism and validity of experimental advertising research.

An open dataset for oracle bone character recognition and decipherment

Oracle bone script, one of the earliest known forms of ancient Chinese writing, presents invaluable research materials for scholars studying the humanities and geography of the Shang Dynasty, dating back 3,000 years. The immense historical and cultural significance of these writings cannot be overstated. However, the passage of time has obscured much of their meaning, presenting a significant challenge in deciphering these ancient texts. With the advent of Artificial Intelligence (AI), employing AI to assist in deciphering Oracle Bone Characters (OBCs) has become a feasible option. Yet, progress in this area has been hindered by a lack of high-quality datasets. To address this issue, this paper details the creation of the HUST-OBC dataset. This dataset encompasses 77,064 images of 1,588 individual deciphered characters and 62,989 images of 9,411 undeciphered characters, with a total of 140,053 images, compiled from diverse sources. The hope is that this dataset could inspire and assist future research in deciphering those unknown OBCs. All the codes and datasets are available at https://github.com/Pengjie-W/HUST-OBC.

The Future of Marketing and Marketing Education

Employers expect university graduates seeking entry-level marketing jobs to be well-versed in contemporary topics, such as sustainable development, digital marketing, big data, analytics, and the role of artificial intelligence (AI) in both traditional and contemporary marketing domains. Because many of today’s cutting-edge technological advances are deeply relevant to marketing, marketing educators must reconsider how they prepare marketing students to enter the technology-enabled world and workforce. The authors propose that marketing educators adapt their teaching of foundational marketing concepts to reflect the technology-augmented marketing era. Such reconsiderations span multiple arenas, including how classes are conducted, which topics are covered, how assignments are crafted, and how technology—and AI and generative artificial intelligence (gen AI) in particular—will transform future marketing roles. The authors also suggest ways educators can modify and reimagine existing marketing courses to prepare students for a successful entry into technology-enabled marketing jobs, as exemplified with some sample class assignments.

Ethical dimensions of generative AI: a cross-domain analysis using machine learning structural topic modeling

Purpose The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI). Design/methodology/approach Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research. Findings The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices. Research limitations/implications This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields. Originality/value The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

Effect of disclosing AI-generated content on prosocial advertising evaluation

With advancements in generative artificial intelligence (AI) technology, there is a growing concern about its ethical implications, transparency, and consumer reactions to AI-generated content. Building on the persuasion knowledge model and algorithm aversion literature, this study explores the effects of AI disclosure in prosocial advertising on consumer attitudes and donation intentions. The findings of Study 1 indicate that the initial disclosure of AI-generated content leads to unfavourable attitudes towards ads, with perceived ad credibility serving as a mediating factor. In Study 2, participants who perceived AI as more human-like rather than machine-like tended to experience a diminished negative impact of AI disclosure. Study 3 also highlights the crucial role of perceived ad credibility in influencing donation intentions following the disclosure of AI-generated content. The theoretical and practical implications of our findings for social marketers and nonprofit organizations are discussed further.

Evaluating the influence of Nano-GO concrete pavement mechanical properties on road performance and traffic safety using ANN-GA and PSO techniques

The burgeoning demand for durable and eco-friendly road infrastructure necessitates the exploration of innovative materials and methodologies. This study investigates the potential of Graphene Oxide (GO), a nano-material known for its exceptional dispersibility and mechanical reinforcement capabilities, to enhance the sustainability and durability of concrete pavements. Leveraging the synergy between advanced artificial intelligence techniques—Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)—it is aimed to delve into the intricate effects of Nano-GO on concrete's mechanical properties. The empirical analysis, underpinned by a comparative evaluation of ANN-GA and ANN-PSO models, reveals that the ANN-GA model excels with a minimal forecast error of 2.73%, underscoring its efficacy in capturing the nuanced interactions between GO and cementitious materials. An optimal concentration is identified through meticulous experimentation across varied Nano-GO dosages that amplify concrete's compressive, flexural, and tensile strengths without compromising workability. This optimal dosage enhances the initial strength significantly, and positions GO as a cornerstone for next-generation premium-grade pavement concretes. The findings advocate for the further exploration and eventual integration of GO in road construction projects, aiming to bolster ecological sustainability and propel the adoption of a circular economy in infrastructure development.

Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists.

In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artificial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias. Generative AI text-to-image production using DALL-E 3 (OpenAI)is readily accessible and user-friendly but may reinforce gender and ethnicity biases. In March 2024, DALL-E 3 was utilized to generate individual and group images of Australian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evaluation of which 30 were individual characters and the remaining 10 images were comprised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitus. Discrepancies in responses were resolved by third-observer consensus. Collectively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29.7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian pharmacists (P < .001). Among the images of individual pharmacists, DALL-E 3 generated 100% as men and 100% were light skin tone. This evaluation reveals the gender and ethnicity bias associated with generative AI text-to-image generation using DALL-E 3 among Australian pharmacists. Generated images have a disproportionately high representation of white men as pharmacists which is not representative of the diversity of pharmacists in Australia today.

The global risks of using chat-bots сontrolled by artificial intelligence

The role and importance of artificial intelligence are defined. The prerequisites and trends of the use of artificial intelligence technologies in the development of chatbots are revealed. The necessity of creations the criteria and indicators for evaluating the effectiveness of chatbots controlled by artificial intelligence are focused. The risks of using artificial intelligence technologies in chatbots, namely: risks of discrimination; cyber security risks; risks to confidential data; ethical risks, validation risks are detailed. The conditions, features and algorithms for using chatbots with the use of artificial intelligence are defined. The conditions, features and algorithms for using chatbots with the use of artificial intelligence are defined. Based on the results of the research, it was concluded that the introduction of chatbots based on generative artificial intelligence has both advantages and global risks associated with the criminal and illegal use of these technologies. The threats and dangers provoked by the chatbots based on artificial intelligence and their consequences are revealed. The directions to minimize the risks of using chatbots controlled by artificial intelligence have been determined.

Evaluating the Capabilities of Generative AI Tools in Understanding Medical Papers: Qualitative Study.

Reading medical papers is a challenging and time-consuming task for doctors, especially when the papers are long and complex. A tool that can help doctors efficiently process and understand medical papers is needed. This study aims to critically assess and compare the comprehension capabilities of large language models (LLMs) in accurately and efficiently understanding medical research papers using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist, which provides a standardized framework for evaluating key elements of observational study. The study is a methodological type of research. The study aims to evaluate the understanding capabilities of new generative artificial intelligence tools in medical papers. A novel benchmark pipeline processed 50 medical research papers from PubMed, comparing the answers of 6 LLMs (GPT-3.5-Turbo, GPT-4-0613, GPT-4-1106, PaLM 2, Claude v1, and Gemini Pro) to the benchmark established by expert medical professors. Fifteen questions, derived from the STROBE checklist, assessed LLMs' understanding of different sections of a research paper. LLMs exhibited varying performance, with GPT-3.5-Turbo achieving the highest percentage of correct answers (n=3916, 66.9%), followed by GPT-4-1106 (n=3837, 65.6%), PaLM 2 (n=3632, 62.1%), Claude v1 (n=2887, 58.3%), Gemini Pro (n=2878, 49.2%), and GPT-4-0613 (n=2580, 44.1%). Statistical analysis revealed statistically significant differences between LLMs (P<.001), with older models showing inconsistent performance compared to newer versions. LLMs showcased distinct performances for each question across different parts of a scholarly paper-with certain models like PaLM 2 and GPT-3.5 showing remarkable versatility and depth in understanding. This study is the first to evaluate the performance of different LLMs in understanding medical papers using the retrieval augmented generation method. The findings highlight the potential of LLMs to enhance medical research by improving efficiency and facilitating evidence-based decision-making. Further research is needed to address limitations such as the influence of question formats, potential biases, and the rapid evolution of LLM models.

Transforming learning experiences and assessments through AI‐empowered cocreation of quality feedback

AbstractThis article examines the transformative impact of generative artificial intelligence (GenAI) in enhancing feedback quality in a Bachelor of Science course. It the challenges of providing personalized, timely feedback to students in larger educational settings, focusing on the use of GenAI to analyze and respond to student logbooks. These logbooks are key to reflective learning and capture students’ insights and progress. The study explores the integration of GenAI in feedback provision, which moves beyond traditional automated processes to tailor feedback to individual student journeys, thus fostering a growth mindset. GenAI shows potential for revolutionizing learning feedback, with positive effects on student engagement and learning outcomes. The article details the innovation, implementation, and context of this approach, reflecting on the learning coach's experience and successes. It links these findings to educational theories, discussing the broader implications for educators, future AI integration in education, and policy considerations. The conclusion outlines the benefits of GenAI in feedback processes and future directions for exploration and ethical guidelines.

Innovations in Pharmacovigilance: Leveraging Artificial Intelligence for Enhanced Drug Safety Monitoring

The technique of pharmacovigilance, which involves keeping an eye on how medical pharmaceuticals are working after they've been approved for use, is vital in making sure that drugs are safe to use. Novel approaches brought about by the advent of Artificial Intelligence (AI) have the potential to greatly improve pharmacovigilance by making ADR identification, evaluation, and prevention much more effective. This study delves into the most recent advancements in AI-driven pharmacovigilance, shedding light on how drug safety monitoring is being revolutionised by machine learning algorithms, natural language processing, and big data analytics. In this article, we will go over the pros, cons, and possible next steps for pharmacovigilance frameworks that use AI.

Understanding the interconnection between assessment of student learning and generative artificial intelligence

Understanding the interconnection between assessment of student learning and generative artificial intelligence

Harmonizing Code and Canvas: The Role of Generative AI in Aesthetic Education for Vocational Students

This paper delves into the pivotal role of generative artificial intelligence (AI) within vocational aesthetic education, specifically focusing on its capacity to augment artistic expression and cultivate technical proficiency. As AI undergoes continuous evolution, it catalyzes significant transformations in educational paradigms by amalgamating creativity with digital acumen, thereby equipping the workforce with adeptness in navigating technologically driven landscapes. The study critically examines how AI reconfigures educational frameworks, enriching learning experiences through tailored, collaborative, and globally oriented approaches. Additionally, it scrutinizes the ethical, pragmatic, and pedagogical hurdles intrinsic to AI integration, encompassing concerns such as data privacy, bias mitigation, and the imperative for perpetual curriculum innovation and educator empowerment. The findings underscore that the realization of AI’s potential in advancing vocational aesthetic education hinges upon strategic deployment, ongoing evaluation, and inclusive discourse among all educational stakeholders, ensuring alignment with educational objectives and responsiveness to global labor market exigencies. This analysis underscores AI’s capacity to optimize educational outcomes and equip students for a multifaceted and evolving future, advocating for proactive and inclusive strategies to harness AI’s advantages within educational milieus.