A Comparative Analysis of Generative AI Traffic Across Continents

This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education.

Aim/Purpose: This paper investigates how academics in Egyptian higher education adopt and engage with generative AI tools, addressing the limited understanding of faculty perceptions and the role of technophobia in influencing adoption. Background: Existing research on generative AI adoption primarily focuses on a single tool (e.g., ChatGPT) and overlooks broader organizational and psychological factors. This study extends the Technology Acceptance Model (TAM) to include technophobia and organizational innovative culture, providing a comprehensive explanation of adoption behaviors in the Egyptian higher education context. Methodology: A mixed-methods design was employed. Quantitative data were collected from 195 academics via a structured survey measuring TAM constructs, technophobia, and organizational culture. Qualitative data were obtained through semi-structured interviews to capture experiences, perceived benefits, and concerns regarding generative AI tools. Contribution: The study refines TAM by demonstrating that technophobia indirectly affects adoption through perceived usefulness and perceived ease of use, while organizational innovative culture does not moderate adoption relationships. It offers both theoretical insights and practical guidance for the responsible use of generative AI in higher education. Findings: Perceived usefulness was the strongest predictor of adoption intention, whereas perceived ease of use was not significant. Technophobia reduced perceived usefulness and ease of use but did not directly affect adoption intention. Organizational innovative culture did not moderate relationships. Interviews highlighted efficiency benefits of generative AI alongside concerns about ethics, originality, and policy gaps. Recommendations for Practitioners: Universities should establish clear policies for the use of generative AI in teaching, assessment, and research, and provide regular training and awareness programs to support responsible adoption. Institutions should encourage critical and purposeful use rather than dependence on generative AI. Recommendation for Researchers: Future studies may expand TAM by including constructs such as trust, perceived risk, and institutional policy support, explore discipline-specific adoption patterns, and examine long-term impacts on teaching and learning. Impact on Society: Generative AI has the potential to enhance academic productivity while raising ethical and integrity concerns. Balanced and responsible implementation can maintain the educational and social mission of universities. Future Research: Further research should involve a wider range of institutions, consider moderators such as digital literacy and organizational readiness, and develop ethical and pedagogical frameworks for the constructive use of AI in higher education.

This research looks at the potential effects of generative artificial intelligence AI on the country's media landscape. Given their pervasiveness, it aims to reveal how AI-powered technologies in media content creation, distribution, and personalisation contribute to the overall process of national progress. Using well-designed questionnaires, the study quantitatively collects data from media professionals, techies, and communication scholars in large cities throughout China. Using statistical tools such as structural equation modelling and regression analysis, one investigated the interplay between the rate of modernisation, the effects of national development, and AI-driven media innovation. Media indices of generative AI demonstrate a clear positive correlation with the effect of modernism and national development programs. As China strives to digitally change its communication infrastructure and increase its cultural influence, technological prowess, and media production, generative AI is playing an increasingly crucial role. This study shows that AI in media may lead to more dynamic stories, practical audience participation, and worldwide outreach, all thanks to modernist techniques. There is no part of this that does not contribute to the advancement of national development goals. The results provide policymakers, media outlets, and AI developers with valuable information for formulating strategies to integrate AI with sustainable development objectives. Via an experimental interaction between generative AI and national development perceived via a modernist lens, this study provides a framework for future research on new media technologies and national change. The discussion of the societal potential presented by AI may now begin.

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 explored students’ perspectives on the benefits and challenges of integrating Generative AI (GenAI) tools in teaching, learning, and assessment within five colleges under the Royal University of Bhutan (RUB). The study is timely and relevant as educators and students in Bhutan increasingly adopt GenAI tools, reflecting a broader global trend. The study outlines three primary objectives. First, it seeks students’ perspectives regarding the integration of GenAI tools in teaching, learning, and assessment in higher education, highlighting the GenAI tools used, the benefits they offer, and the challenges they may impose. Second, the study aspires to provide practical recommendations for educators and academics in higher education institutions for the effective integration of GenAI tools. Finally, the study aims to provide recommendations to relevant stakeholders and policymakers for the adoption or expansion of AI initiatives within the precincts of RUB colleges. This study employed a qualitative approach as qualitative research allows for a deeper understanding of human behaviour and experiences. A purposive sampling technique was used to ensure that participants were selected based on their potential to provide valuable insights relevant to the research objectives. Data were collected through semi-structured focus group interviews, with each group comprising six members (three male and three female). A total of 180 students participated across 30 focus group interviews conducted in five colleges, with six focus group interviews in each college. The data collected were transcribed, coded, and categorised into themes for analysis. To maintain confidentiality, the researchers used a systematic labelling system for both focus groups and individual students during the data analysis process, enabling a comprehensive and structured interpretation of the findings. The findings revealed key benefits of GenAI, such as providing quick and accessible information, fostering personalised learning, and offering emotional support. However, the findings also revealed challenges in terms of the reliability of AI-generated content, the potential hindrance to creativity and critical thinking, and the risk of social and emotional disconnect between students and their learning communities due to overreliance on GenAI. The study therefore recommends raising awareness and improving understanding of GenAI tools among students and establishing comprehensive policy frameworks to promote their ethical use across RUB colleges while upholding high standards of academic quality.

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] Generative AI tools, including the popular ChatGPT, have had a significant impact on discourses about future work and educational practices. Previous research in science education has highlighted the potential of generative AI in various education-related areas, including generating valuable discussion material, solving physics problems, and acting as a tutor. However, little research has been conducted on the role of generative AI tools in laboratory work, an essential part of science education, and physics education specifically. Here, we show various ways in which high school students use ChatGPT during a physics laboratory session and discuss the relevance of using generative AI tools to investigate acoustic levitation and the speed of sound in air. Additionally, employing variation theory as a theoretical lens in the analysis, we identify how generative AI can be used to further develop students’ problem-solving skills in the physics laboratory. However, although our study shows that generative AI tools may handle some relevant questions and problems during laboratory work, the teacher still plays a crucial role in identifying students’ needs and capabilities of understanding the potential and limitations of generative AI. Finally, this study serves as an important point of discussion regarding the ways in which students need support and training to efficiently utilize generative AI to further their learning of physics.

Since ChatGPT's release, there has been confusion about how generative AI (GenAI) tools can be responsibly utilized in TESOL research processes. While there are many examples of inappropriate uses of GenAI, there are also growing examples of its potential utility. In addition, GenAI use has become normalized in many TESOL scholars' academic writing and knowledge‐production activities. The rapid adoption of GenAI into researchers' practices has outpaced our understanding of ethical appropriateness and empirical explorations into the tools' efficacies in supporting research tasks. Responding to a lack of standards and guidance in the TESOL field regarding the use of GenAI in our research activities, we propose a disciplinary framework for using GenAI in TESOL research. This framework, built on four elements of quality study proposed by Plonsky (2024): (1) transparency, (2) methodological rigor, (3) ethics, and (4) societal value; with an additional element proposed by us, (5) human accountability, can assist scholars in making more informed decisions about the use of GenAI at different stages of their research process—from conceptualization to dissemination. The article also provides important considerations that scholars must know if they plan to implement GenAI in their research process. The framework is useful for journal editors and reviewers to evaluate the use of GenAI in TESOL research. We end the article with a call for the TESOL community to adapt our framework to meet their needs.

Aim/Purpose To address the gap in students’ effective use of generative AI tools, this paper presents a framework to introduce university students to the principles and practices of prompt engineering – the art and science of crafting precise and purposeful inputs to guide LLMs in generating accurate and useful outputs. This paper aims to equip students with strategies to interact meaningfully with AI chatbots for academic success. Background Generative AI tools, like ChatGPT, are widely adopted in educational settings, yet many students lack the skills to harness their full potential. This paper introduces prompt engineering as a critical competency for students to develop both technical proficiency and critical thinking. Methodology The paper provides a structured framework for teaching prompt engineering in university courses. It draws on existing literature, practical applications, and pedagogical strategies to guide educators in integrating generative AI effectively into their university courses. Contribution This paper contributes to the body of knowledge by presenting a comprehensive framework for teaching prompt engineering. It highlights prompt engineering’s role in enhancing AI literacy and preparing students for technology-driven academic and professional environments. Findings Prompt engineering enhances students’ ability to generate precise and relevant outputs from AI tools by supporting student development of communication strategies tailored to large language models. This guide introduces essential concepts and skills that facilitate effective interaction with AI chatbots. Structured instruction in prompt engineering helps to foster critical thinking, problem-solving, and reflective interaction – key competencies for navigating an AI-driven environment. Additionally, integrating prompt engineering into education improves AI literacy, enabling students to tackle complex tasks and apply AI tools effectively across various disciplines. Recommendations for Practitioners Educators should integrate structured, prompt engineering instruction into their courses, emphasizing its interdisciplinary applications. Scaffolded learning will help students develop competency in applying prompt engineering techniques and strategies. Recommendations for Researchers Future studies should explore the long-term impact of prompt engineering instruction on academic performance and professional readiness. Additionally, research should examine its effectiveness across diverse disciplines. Impact on Society Teaching prompt engineering equips students with essential AI literacy skills, fostering responsible and innovative use of AI in academic, professional, and societal contexts. This contributes to a workforce better prepared for the challenges of the AI era. Future Research Further research should examine the integration of multimodal AI tools alongside prompt engineering to assess how combined approaches can enhance learning outcomes. In addition, studies should investigate the effective-ness of various instructional designs to identify best practices for promoting student engagement and skill development. Exploring discipline-specific and pedagogically meaningful student use cases will also be essential to guiding the thoughtful integration of AI tools across diverse educational contexts.

Objectives This study investigates the adoption and integration of Generative AI (GAI) technologies in daily life, focusing on factors that influence user behavior and attitudes. Method Using a mixed‐methods approach, we combined quantitative, qualitative, and semi‐experimental methodologies to capture the complexities of GAI tool usage. A total of 897 individuals completed an online survey with both closed and open‐ended questions; additionally, 38 students participated in a semi‐experimental stage to assess the impact of an online course, “GAI: From Theory to Practice.” Results Key findings underscore the role of personality traits such as openness and self‐efficacy in promoting GAI usage, mediated by behavioural perceptions and digital engagement patterns. In line with the UTAUT2 model, Performance and Effort Expectancy were the strongest predictors of GAI usage. Social media engagement and information overload management also emerged as predictors, illustrating the interplay between digital behaviours and GAI adoption. Qualitative findings revealed a nuanced understanding of user perceptions, emotions, and societal needs, uncovering themes of GAI's revolutionary potential, emotional ambivalence, and the demand for regulatory and educational frameworks. The experimental results demonstrated significant improvements in students' perceptions of GAI utility, self‐reported skill levels, and GAI usage across work, study, and personal contexts following the course. Conclusion This research advances understanding of GAI's societal integration and highlights the need for targeted training and regulatory policies to support responsible adoption and enhance digital literacy. It offers practical recommendations for educators, policymakers, and designers of generative AI tools.

Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, AI-generated art is reshaping the art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths.

The TrollLabs Open dataset includes comprehensive information that offers a comparison of design practices and outcomes between human participants and Generative AI during a hackathon event. The dataset was curated through the running of a prototyping hackathon designed to assess the abilities and performance of generative AI, specifically ChatGPT, in the early stages of engineering design. This assessment involved comparing ChatGPT's performance to that of experienced engineering students in a hackathon setting, where participants competed by making a prototype that fires a NERF dart as far as possible. In this setup, all ideas, concepts, strategies, and actions undertaken by the AI-controlled team were autonomously generated by the ChatGPT, without human intervention or guidance, but implemented by two participants. Five self-directed baseline teams competed against the AI team. The dataset comprises 116 prototype entries and 433 edges (connection) that enable comparative analysis of design practices and performance between the team instructed solely by generative AI and baseline teams of experienced engineering design students. Prototypes and their attribute data were captured using Pro2booth, an online prototype capture platform running on participants' phones and computers. The dataset includes a transcript of the conversation between ChatGPT and the team responsible for implementing its recommendations, featuring 97 exchanges of prompts and responses. It contains the initial prompt used to instruct the AI, the objective and rules of the hackathon and the objective performance of teams, showing the ChatGPT team finishing 2nd among six teams. To the authors' knowledge, the TrollLabs Open dataset is the first and only open resource that directly compares the performance of generative AI with human teams in an engineering design context. Thus, it is intended to be a valuable resource to design researchers, engineering and design students, educators, and industry professionals seeking to find strategies for implementing generative AI tools in their design processes. By offering a comprehensive data collection, the dataset enables external researchers to conduct in-depth analyses that could highlight the practical implications of integrating generative AI in design practices, possibly providing an overview of its limitations and presenting recommendations for improved integration in the design process.

Aim/Purpose: This study investigated how undergraduate and graduate students in a teacher education program perceived, used, and ethically reflected upon generative AI tools. The problem is the unclear state of preservice teachers’ understanding and use of generative AI in educational settings, which this study aimed to explain. Background: Given generative AI’s growing presence in education, this study addressed a gap in understanding by examining preservice teachers’ perceptions, experiences, and ethical concerns, particularly in relation to self-regulated learning. Methodology: A convergent parallel mixed-methods design was used to gather quantitative and qualitative data from 73 students enrolled in a teacher education program at the University of Nevada, Las Vegas. Descriptive statistics, correlations, and thematic analysis were used for data analysis. Contribution: This study contributes to the emerging literature by providing insights into preservice teachers’ usage patterns of generative AI, the relationship between AI use and self-regulatory skills, and a detailed examination of ethical concerns, informing effective AI integration into teacher education programs. Findings: Text-based generative AI was the most familiar tool among participants. The primary reasons for the use included editing papers, searching for new ideas, and understanding course material. A significant positive correlation was identified between information search with the help of generative AI and resource management skill. Key themes identified by thematic analysis included perceived benefits for student engagement and creativity, AI’s potential as a writing assistant, and ethical concerns about plagiarism and misuse. Overall, preservice teachers demonstrated surface-level use and a general lack of in-depth knowledge about AI integration. Recommendations for Practitioners: Teacher educators should provide explicit training in generative AI, including practical guidelines and clear ethical frameworks to foster effective and responsible integration into classroom practice. Recommendation for Researchers: Future researchers should develop validated measurement instruments specifically designed for studying generative AI perceptions and usage and explore longitudinal impacts as preservice teachers transition into professional teaching roles. Impact on Society: By guiding preservice teachers toward informed and ethical use of generative AI, this study supports broader societal goals of promoting digital literacy, ethical technology integration, and enhanced learning experiences for future generations. Future Research: Subsequent research should further explore reasons behind the limited adoption of generative AI among preservice teachers, examine the long-term impacts of AI use on pedagogical practices, and investigate structured frameworks for promoting deeper AI integration within the teaching and learning domain.

An ASEE 2023 paper considered whether student use of Generative Artificial Intelligence (GenAI) to write engineering papers constitutes cheating. It was concluded that it depended on specific circumstances, but it was noted that students might potentially undermine their own learning by relying on tools like ChatGPT to answer questions and compose papers. This paper addresses how to enable student use of these tools in a way that students are not cheating themselves. Educators faced a related issue 20 years ago when calculators were introduced into the classroom. As time passed, instructors realized that allowing students to use calculators allowed them to concentrate on teaching concepts, strategies for problem solving and critical thinking. Calculators allowed more individualized instruction and more use of real-world applications. The introduction of calculators into the classroom stimulated discussion on ethical use of technology in teaching. A similar revolution is occurring with the introduction of Generative Artificial Intelligence tools such as ChatGPT, BARD and many others, and a similar set of opportunities is emerging. A key issue is how to use GenAI tools constructively to encourage critical thinking in the solving of engineering problems. The tools can make it easier to differentiate instruction to meet the diverse needs of students and tailor teaching to students with different levels of skills. Using Generative AI tools can help build students' confidence in their own abilities to function as engineers. For example, students can analyze a problem from a variety of perspectives by varying the prompts they give to their GenAI tool. In this paper, we discuss some actions taken in our classes, including changing the style of assessments, teaching students "prompt engineering" as part of the critical thinking process, teaching students how to use Generative AI effectively in coding, and helping students to acquire the habit of validating outputs from Gen AI tools to eliminate hallucinations and fake references. Students can work at their own pace and explore concepts on their own, as Gen AI tools provide instant feedback. Students can explore a problem from multiple perspectives, including the impact of various ethical considerations by varying their prompts. Using Gen AI to generate simple answers to simple questions could, indeed, cheat students out of the ability to learn important engineering concepts. Using Gen AI to explore alternatives and assess different approaches to an engineering problem, for example asking for code using different Python libraries, can make students better and more effective engineers.

This study examines the impact of generative artificial intelligence technologies on the digital representation of cultural heritage within the context of visual communication design. The primary objective is to explore how text-to-image AI tools interpret and reproduce cultural motifs, symbols, and aesthetic elements. The methodology involved selecting specific motifs from Anatolian cultural heritage and generating visual outputs using different generative AI platforms, including Midjourney, DALL-E, and Stable Diffusion, based on various text prompts. The resulting images were analyzed through content analysis, focusing on fundamental design principles such as color, composition, texture, form, and symbolic meaning. Findings indicate that while generative AI tools offer creative diversity in cultural reinterpretation, they also exhibit contextual limitations in accurately preserving heritage references. The outcomes of the study provide recommendations for ethical, aesthetic, and technical approaches to cultural heritage representation in visual communication design. Future research may focus on strategies for safeguarding cultural heritage and developing sustainable models for its digital representation through generative AI.

ChatGPT or Bard: Who is a better Certified Ethical Hacker?