Original Content Research Articles

A questionnaire to assess ecoanxiety and its sociodemographic determinants among Italian adolescents

Abstract Background Climate change poses serious challenges to Public Health, including mental health impacts. In Italy, few studies investigated the rise of eco-anxiety, especially among adolescents, who are the main group affected by this feeling. Specific measurement tools for this population are needed to fill this gap. The aim is to adapt the Italian version of the Climate Change Anxiety Scale (CCAS) to adolescents, and pair it with a socioeconomic and demographic questionnaire (SDQ) to assess determinants. Methods The Italian CCAS was modified for adolescents through a process involving a literature review, expert discussions, and a pretest. This adapted version (a-CCAS) was tested with individuals aged 14-18 years old, maintaining an ideal ratio of 10 respondents per item. Extant psychometric scales were utilized to assess its validity. The SDQ was developed from results of a systematic review of known associations between socioeconomic and demographic factors and eco-anxiety in youth, and expert discussions, and validated by piloting the SDQ with another sample of adolescents. Internal consistency of the a-CCAS and SDQ was assessed by Cronbach’s alpha (Cα). Results The a-CCAS kept most of its original content, comprising 13 items and 2 subscales: cognitive and functional impairment. Pilot respondents were 162; Cα coefficient was >0.7 for both subscales. Convergent and concurrent validity showed positive correlations with the extant scales. The SDQ comprised 12 items in 3 domains: family, social and political-environmental determinants. Respondents were 47 and Cα coefficient was >0.7. Conclusions The a-CCAS and SDQ are valid tools for assessing baseline eco-anxiety and its determinants in Italian adolescents and can contribute to Public Health efforts to reduce the climate-related mental health burden, by measuring the impact of specific educational interventions about climate change targeted at the young. This questionnaire was created as part of the PRIN P2022JJ3LB project. Key messages • These results contribute to filling an evidence gap on eco-anxiety and its socioeconomic and demographic determinants among Italian adolescents by providing researchers with a valid measurement tool. • This tool holds promise for assessing the impact of psychologically-informed educational interventions about climate change and adaptive coping strategies on adolescents’ eco-anxiety levels.

Keeping viewers hooked: Netflix’s innovative strategies for reducing churn

Netflix’s digital disruption in the entertainment industry has been significantly enhanced by digital nudging, which refers to using behavioural science techniques to subtly influence user decisions (Thaler and Sunstein, 2008). By leveraging data analytics, Netflix personalises the viewing experience, guiding users toward content they are likely to enjoy, which increases engagement and encourages binge-watching. The platform employs sophisticated algorithms to recommend titles, effectively nudging viewers to explore new genres or original content based on their viewing history (Lobato, 2019). This strategy has been pivotal in fostering a binge-watching culture, contrasting sharply with traditional broadcasting schedules offering weekly instalments. Netflix’s ability to analyse user data allows it to create content that aligns with viewer preferences, further enhancing its appeal. By understanding what resonates with audiences, Netflix can produce targeted original content that captures attention and retains subscribers. Digital nudging plays a crucial role in Netflix’s strategy, reinforcing its position as a leader in the streaming market while reshaping consumption habits across the industry. However, digital nudging presents both opportunities and challenges for customer churn management, raising critical ethical questions about autonomy and manipulation. Finding the right balance between effective customer retention strategies and ethical considerations will be vital for fostering trust and enhancing the overall user experience on the platform.

The Impact of Generative Artificial Intelligence on Education: A Comparative Study

Generative Artificial Intelligence (GAI), also known as creative AI, is a technology capable of independently producing original and creative content, such as text, images, videos, music, or code. It uses machine learning algorithms to analyze and learn from a vast amount of data to generate similar or even innovative content. This type of AI is used in several fields, such as health for the production of new drugs, design to generate adaptive designs, video games to create immersive environments, and artificial intelligence to generate training data for new networks. Seizing the opportunities offered by AI innovations in education is crucial. We cannot afford to let time pass without doing anything and without benefiting from this technique. It is time to enhance traditional learning methods and adopt more innovative approaches that take advantage of the potential of AI, like content and exam generation, and also improve students' learning experience by exploiting their closeness to the technology. This paper aims to answer the question of when and how to use artificial intelligence responsibly, considering it as a technology and limiting its excessive use. Additionally, we present the impact of GAI through a comparative study of student performance in three exam types: a classical MCQ exam written by the teacher, an adaptive MCQ exam generated by GAI from a topic, and a third MCQ exam generated by GAI based on the content of the provided documents. This study is conducted on students undergoing a software testing training program. By evaluating their performance in the AI-generated adaptive MCQ exams and the traditional MCQ exam, we can gain insights into the potential of this technology to enhance educational practices and provide personalized learning experiences. This research will contribute to the ongoing discussion on responsible and effective utilization of GAI in education, paving the way for future advancements in the field.

CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models

Voice conversion (VC) refers to the technique of modifying one speaker’s voice to mimic another’s while retaining the original linguistic content. This technology finds its applications in fields such as speech synthesis, accent modification, medicine, security, privacy, and entertainment. Among the various deep generative models used for voice conversion, including variational autoencoders (VAEs) and generative adversarial networks (GANs), diffusion models (DMs) have recently gained attention as promising methods due to their training stability and strong performance in data generation. Nevertheless, traditional DMs focus mainly on learning reconstruction paths like VAEs, rather than conversion paths as GANs do, thereby restricting the quality of the converted speech. To overcome this limitation and enhance voice conversion performance, we propose a cycle-consistent diffusion (CycleDiffusion) model, which comprises two DMs: one for converting the source speaker’s voice to the target speaker’s voice and the other for converting it back to the source speaker’s voice. By employing two DMs and enforcing a cycle consistency loss, the CycleDiffusion model effectively learns both reconstruction and conversion paths, producing high-quality converted speech. The effectiveness of the proposed model in voice conversion is validated through experiments using the VCTK (Voice Cloning Toolkit) dataset.

Financial Development of Disney: Analyzing Box Office Revenue, Derivative Products, and Audience Demographics

Disney is known around the world for its unique animated films, theme parks and classic characters. Through continuous innovation and development, Disney has combined The traditional art of animation with modern technology to create many classic animated films such as "The Lion King," "Frozen" and "Beauty and the Beast."This paper explores Disney's success factors in terms of box office revenue, derivative products, and audience demographiAnalyzingzing relevant literature and it summarizes how Disney has solidified its leading position in the global entertainment industry through high-grossing films, a wide range of derivative products, and precise market positioning. The study reveals that Disney's continuous innovation and market adaptation, including the introduction of new content and diverse products, have successfully attracted audiences of all ages. The paper also provides recommendations for further enhancing Disney's market position and audience engagement, such as investing in original content, emphasizing diversity and inclusivity, and expanding digital and interactive experiences.

Classification and Comparison of Data Augmentation Techniques

Data augmentation is a technical method that generates additional data samples by applying different transformations and processes to the original training data. It aims to increase the size and diversity of the dataset, thus improving the models' generalization ability. This paper provides a detailed introduction to geometric transformations, data mixing, automated data augmentation methods, and generative adversarial methods. Geometric transformations manipulate the spatial configuration of data samples to create new variants that retain the original content. Data mixing techniques blend multiple data samples to produce novel training examples, promoting robustness and reducing overfitting. Automated data augmentation methods leverage search algorithms to discover optimal augmentation policies that improve model performance without manual intervention. Generative adversarial methods employ generative adversarial networks to synthesize realistic data samples that enrich the training dataset. These methods find broad application in domains including object detection, image classification, natural language processing, and speech recognition, significantly improving model performance. The paper explores the advantages and limitations of data augmentation techniques in practical applications and identifies future research directions and challenges. By systematically classifying and selecting appropriate data augmentation methods for specific application scenarios, this review provides a theoretical foundation and practical guidance for real-world applications.

Extract And Reconstruct: Research On the Content Of “Second Creation” Short Videos for Film Promotion

Short videos have surged in popularity, particularly on platforms like Douyin, Kuaishou, WeChat Channel, and Little Red Book in China. These brief yet engaging videos are extensively produced and consumed, making them a potent tool for promoting theatrical films. Among the various types of short videos, second-creation short videos—those that creatively reinterpret or remix original content—are particularly effective in driving platform users to theaters. This research focuses on these second-creation short videos used for film promotion. Through extensive viewing and creating these videos during an internship, the author observed notable deviations in meaning between the original films and their second-creation counterparts. To delve deeper, this study analyzes short videos related to “The Boy and the Heron”. Starting from the unique characteristics of second-creation short videos, the research identifies issues such as the narrowing of meaning that can occur in the adaptation process. By highlighting these problems, the study aims to offer insights into how audiences should interpret short videos in the context of film promotion. Finally, the research proposes recommendations for better alignment between original films and their promotional short videos, ensuring a more accurate representation of the cinematic works.

Investing in Netflix: Accounting for Content Assets*

ABSTRACTThis case describes an equity investment scenario in the context of the COVID‐19 pandemic. A fictional decision‐maker, Amandeep Singh, is considering whether or not to invest in Netflix, Inc. (Netflix). Netflix has benefited from changing habits during the pandemic, but Singh questions whether those benefits will persist post‐pandemic. To make his decision, Singh needs to perform a qualitative analysis of the video streaming industry and Netflix itself before moving to a quantitative analysis of Netflix's performance. To properly assess performance, Singh needs to fully understand Netflix's accounting methods and estimates for content assets—especially original content. The case is ideally suited to an MBA or Executive MBA audience. Students can easily engage with the case since they will be familiar with the company and industry. This familiarity allows for a general discussion about the industry's future and key success factors. The case provides an opportunity to cover core financial statement analysis concepts and drives home the importance of understanding accounting judgments when assessing firm performance.

Edge Caching Placement Strategy based on Evolutionary Game for Conversational Information Seeking in Edge Cloud Computing

In Internet applications, network conversation is the primary communication between the user and server. The server needs to efficiently and quickly return the corresponding service according to the conversation sent by the user to improve the users’ Quality of Service. Thus, Conversation Information Seeking (CIS) research has become a hot topic today. In Cloud Computing (CC), a central service mode, the conversation is transmitted between the user and the remote cloud over a long distance. With the explosive growth of Internet applications, network congestion, long-distance communication, and single point of failure have brought new challenges to the centralized service mode. People put forward Edge Cloud Computing (ECC) to meet the new challenges of the centralized service mode of CC. As a distributed service mode, ECC is an extension of CC. By migrating services from the remote cloud to the network edge closer to users, ECC can solve the above challenges in CC well. In ECC, people solve the problem of CIS through edge caching. The current research focuses on designing the edge cache strategy to achieve more predictable caching. In this article, we propose an edge cache placement method Evolutionary Game based Caching Placement Strategy (EG-CPS). This method consists of three modules: the user preference prediction module, the content popularity calculation module, and the cache placement decision module. To maximize the predictability of the cache strategy, we are committed to optimizing the cache hit rate and service latency. The simulation experiment compares the proposed strategy with several other cache strategies. The experimental results illustrate that EG-CPS can reduce up to 2.4% of the original average content request latency, increase the average direct cache hit rate by 1.7%, and increase the average edge cache hit rate by 3.3%.

OVERLOOKING THE TRANSFORMATION OF VALUES THROUGH THE TRANSNATIONAL AMERICAN REMAKES MOVIES

This study explores the transformation of values in transnational remake movies, focusing on their popularity and the incorporation of new elements to suit target audience preferences. An analysis of The Lake House, a remake of the Korean film Il Mare, reveals shifts in values and the integration of American cultural aspects. These changes stem from audience demands, business considerations, and Hollywood's global influence. Remakes require more than simply reproducing original content; they involve capturing cultural nuances from both versions. Utilizing Hall's representation approach and qualitative methods, the study assesses transnational remakes and their conveyed values. The research identifies shifts from South Korean values to those of the American target market, such as the transition from interdependence to self-reliance and a preference for directness, assertion, and rationality. The American film industry should carefully evaluate the suitability of movies for its audiences, recognizing changes in scenes and messages. The establishment of American values in remakes is influenced by the success of original films and their formulas. As transnational products, movies must be repackaged for target audiences. The industry faces complex processes to align the values of popular films with audience tastes while maintaining the essence of original works.

CNVs in Patients with Neurodevelopmental Disorders: Meta-Analysis

This review is devoted to a comprehensive analysis of DNA copy number variations (CNVs) identified in patients with neurodevelopmental disorders (NDDs) from the literature. The selection of publications was conducted using specifically developed criteria. CNVs were characterized based on their clinical significance, type of copy number alteration (microdeletion/microduplication), size, origin, and gene content. The study sample comprised 3,375 patients with NDDs, among whom pathogenic and likely pathogenic CNVs, as well as variants of uncertain clinical significance, were identified in 395 individuals (12%). Chromosomal variations from each category were identified in 89 (3%), 56 (2%), and 241 (7%) patients, respectively. Nine individuals exhibited combinations of CNVs with varying clinical significance. The number of microduplications slightly exceeded the number of deletions (250 and 204, respectively). The size of most CNVs ranged from 193 bp to 400 kb and from 1 to 3 Mb (237 and 96, respectively). Seventy-two variants originated de novo, while 165 were inherited. Eighty-six CNVs were associated with 33 known microdeletion/microduplication syndromes. The most prevalent syndromes included 1q21.1 microduplication (7/395, 1.8%) (OMIM: 612475), 2p16.3 microdeletion (9/395, 2.3%) (OMIM: 614332), 15q13.3 microdeletion (7/395, 1.8%) (OMIM: 612001), 16p11.2 microdeletion (9/395, 2.3%) (OMIM: 611913), and 22q11.2 microduplications (7/395, 1.8%) (OMIM: 608363). Enrichment analysis revealed that pathogenic CNVs, as well as variants of uncertain clinical significance, are enriched in genes associated with abnormal behavioral/neurological phenotypes. Likely pathogenic CNVs included genes linked to disorders of the nervous system and homeostasis/metabolism.

Preface

The 2024 International Conference on Computational Intelligence and Communication System (CCICS2024), held in Wuhan, China from June 26-28, 2024, is a testament to the ever-evolving nature of fields of computational intelligence and communication technology, has attracted a diverse and brilliant array of researchers, engineers, and scholars from around the globe. CCICS2024 attracted more than 80 participants from around the world, which is a vibrant and engaging event, with a diverse program that included an opening ceremony, keynote speeches, oral presentations, poster sessions, Q&A discussions, and an ending ceremony. The keynote speakers, renowned experts worldwide in their respective fields, provided insightful overviews of the latest developments and trends in domains related to computational intelligence and communication technology. The oral presentations and poster sessions were equally enriching. Researchers and practitioners shared their latest findings, discussed challenges and opportunities, and explored potential collaborations. The lively discussions and interactions among the participants created a vibrant intellectual atmosphere that fostered innovation and knowledge sharing. The submissions received for this conference are vivid evidence of the creativity and innovation that flourishes in related areas. The manuscripts cover a wide range of topics, including computing for emerging technology, signal processing, sensor and detection technology, futuristic communication and network technologies, etc. The diversity and depth of these contributions reflect the vibrant and interdisciplinary nature of relevant industries. All papers submitted to the conference have been reviewed using a double-blind peer-review process. Each paper was tested for similarity with already published content using the Turnitin platform. All sources of similarities were checked and only original content was accepted. This volume of proceedings is beneficial for readers from academia and industry to understand and tackle the challenges efficiently and adopt appropriate solutions in the above-mentioned topics. Building on the success of this event, we hope CCICS 2024 will be one of the most spectacular events in those areas. We would like to thank the reviewers and the members of the international scientific committees. We also extend our sincere thanks to Prof. Joarder Kamruzzaman, Prof. Yangjun Chen, Prof. Oscar Eduardo Ruiz Salguero, Prof. Jie Yang, Prof. JingTao Yao and Dr. Iacovos Ioannou for their wonderful keynote talks. We are grateful to everyone who has contributed to the realization of CCICS 2024. We also look forward to the next conferences in this series, which will continue to build upon the foundation laid by this one. Organizing Committee of CCICS 2024 Prof. Yuriy S. Shmaliy (Conference Chair) IEEE Fellow, AAIA Fellow, University of Guanajuato, Mexico List of Conference Committee is available in this Pdf.

Интеллектуальная обработка текстовой информации: обзор автоматизированных методов суммаризации

Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, developed based on interdisciplinary research in linguistics, computer technologies, and artificial intelligence. It focuses on syntactic and lexical techniques employed by neural network models for text compression. The paper presents performance examples of such AI-powered services as QuillBot, Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT. The contemporary automated models proved effective in using extractive and abstractive methods to generate summaries of varying quality and length. The extractive approach relies on identifying the most significant sentences from the original text, while abstractive algorithms create new sentence structures that preserve the main idea of the original content. Automated summarizers effectively utilize text compression techniques that are inherent to human approach to text processing, e.g., they exclude redundant information, simplify complex structures, and generalize data. These technologies provide high accuracy and coherence in the generated summaries, though each summarization model has its limitations. Optimal results depend on the specifics of the task at hand: extractive models provide brevity and precision while abstractive ones allow for deeper semantic processing. Automated summarization is becoming an important tool in various fields that require effective analysis and processing of large text data.

Once Upon a Time, “About 1.170 Results”: Exploring the Narrative Power of Search Engines

Search engines play a pivotal role in online ecology and can have a significant impact on people, especially in the case of a name search. The pros and cons of removing search results have been at the heart of an ongoing debate revolving around “the right ro be forgotten” (art. 17 of the General Data Protection Regulation). This article proposes to contribute the discussion by philosophically reflecting on the transformative power of search engines in relation to original content. It shows that the implications of search engines run deeper than the mere display of faulty or outdated content. In order to reveal and conceptualise the impact that search engines have on the information they present, this article operationalises Ricoeur’s notion of ‘semantic autonomy’ in a digital context. This offers a framework that is used to analyse the transformative power of search engines across several dimensions: the relation to authors, audiences, the message and the semantic value of content. The article concludes that search engines have a transformative power that may bypass human intentions on multiple levels and, to a certain degree, allows them to ‘tell a story’ on their own.

Advanced fine-tuning procedures to enhance DNN robustness in visual coding for machines

Video Coding for Machines (VCM) is gaining momentum in applications like autonomous driving, industry manufacturing, and surveillance, where the robustness of machine learning algorithms against coding artifacts is one of the key success factors. This work complements the MPEG/JVET standardization efforts in improving the resilience of deep neural network (DNN)-based machine models against such coding artifacts by proposing the following three advanced fine-tuning procedures for their training: (1) the progressive increase of the distortion strength as the training proceeds; (2) the incorporation of a regularization term in the original loss function to minimize the distance between predictions on compressed and original content; and (3) a joint training procedure that combines the proposed two approaches. These proposals were evaluated against a conventional fine-tuning anchor on two different machine tasks and datasets: image classification on ImageNet and semantic segmentation on Cityscapes. Our joint training procedure is shown to reduce the training time in both cases and still obtain a 2.4% coding gain in image classification and 7.4% in semantic segmentation, whereas a slight increase in training time can bring up to 9.4% better coding efficiency for the segmentation. All these coding gains are obtained without any additional inference or encoding time. As these advanced fine-tuning procedures are standard-compliant, they offer the potential to have a significant impact on visual coding for machine applications.

Hydrogen loss from pyroxene within granulite xenoliths at Damaping, North China craton

The preservation of original water contents within nominally anhydrous minerals is essential for understanding the deep Earth’s water budget. Here we present a comprehensive analysis of mineral chemistry and Fourier transform infrared spectroscopy on 15 lower-crustal granulite xenoliths collected from Damaping in the North China craton. Our analyses reveal that the orthopyroxene grains from two samples exhibit hydrogen-deficient rims, suggestive of hydrogen loss. Drawing upon experimentally determined hydrogen diffusivity in pyroxene, we propose that clinopyroxene, despite the absence of evident hydrogen zoning, may have likewise undergone partial hydrogen depletion. Our findings call into question the conventional belief that hydrogen concentrations in pyroxene are faithful proxies for the original water content in the continental lower crust. We attribute the loss of hydrogen in pyroxene to magmatic outgassing, most likely occurring during the surface flow stage. Such a process could partially explain the relatively lower water contents documented in the granulite xenoliths when compared to those found in the Precambrian granulite terranes from the North China craton. Considering recent studies on mantle xenoliths, it becomes evident that both basalt-hosted mantle and lower-crustal xenoliths may have experienced partial loss of their original water contents within the deep Earth.

ST2SI: Image Style Transfer via Vision Transformer using Spatial Interaction

While retaining the original content structure, image style transfer uses style image to render it to obtain stylized images with artistic features. Because the content image contains different detail units and the style image has various style patterns, it is easy to cause the distortion of the stylized image. We proposes a new Style Transfer based on Vision Transformer using Spatial Interaction (ST2SI), which takes advantage of Spatial Interactive Convolution (SIC) and Spatial Unit Attention (SUA) to further enhance the content and style representation, so that the encoder can not only better learn the features of the content domain and the style domain, but also maintain the structural integrity of the image content and the effective integration of style features. Concretely, the high-order spatial interaction ability of Spatial Interactive Convolution can capture complex style patterns, and Spatial Unit Attention can balance the content information of different detail units through the change of attention weight, thus solving the problem of image distortion. Comprehensive qualitative and quantitative experiments prove the efficacy of our approach.

Contingent valuation versus choice experiment: estimating the willingness to pay for organic oranges in Vietnam

PurposeThe study estimates the willingness to pay for organic oranges and identifies its influencing factors among consumers in the Mekong Delta, Vietnam.Design/methodology/approachThe study used survey data from 413 households in the Mekong Delta from March 2022 to July 2022. The choice experiment (CE) and contingent valuation method (CVM) were employed to analyze consumers’ willingness to pay (WTP). STATA 17 software was used to analyze research data in the logit model and mixed logit model.FindingsThe research results from the CVM approach show that a number of demographic characteristics have a direct impact on WTP, such as education, educational attainment, family size, the presence of children and the elderly in the household, food safety and environmental awareness. The CE model shows product attributes that influence consumers’ WTP, such as country of origin, traceability, quality grade, organic certification, ecolabel and organic content. Both approaches show that price is the main barrier to organic orange consumption.Research limitations/implicationsThe study surveyed four large cities in four provinces representing the Mekong Delta region.Practical implicationsOur study helps administrators have a deeper insight into consumer preferences and behavior, specifically the factors that affect consumers' WTP, an important indicator of demand for the success of manufacturers and marketers in developing as well as improving marketing strategies. Knowledge of a product’s WTP on behalf of (potential) customers plays an important role in many areas of marketing management, such as pricing decisions or new product development.Social implicationsFurthermore, this understanding will inform policymakers about the future of agricultural markets in Vietnam and help them better prepare for the making of sustainable agricultural policies. Develop organic agriculture to both protect human health, protect the living environment and protect the soil from degradation, ensuring sustainable agricultural production. This is also one of the measures to help people stay away from diseases to limit the social burden.Originality/valueThe study confirms that both CVM and CE models can be used to estimate WTP. However, CVM fits the overall WTP estimate, while CE is more appropriate when estimating WTP for individual scenarios through combining attributes with different levels.

The creative agency of large language models: a philosophical inquiry

AbstractThis paper explores the difficult question of whether Large Language Models (LLMs) are intrinsically creative. Because they can independently create original content, LLMs are often seen as creative agents. Contrary to the belief that LLMs are creative, this paper argues that LLMs are not creative for two reasons. First, LLMs are not creative because they lack an essential component of creativity, which is the first-person experience of the world. Secondly, LLMs are not creative because they are not the principal authors of their creative output, for they lack the subjective awareness and intentionality necessary to be regarded as authors, and their output is a collaborative effort of the AI model, data providers, and other stakeholders. Since they are not full-fledged authors in a traditional sense, they are not creative.

Generative AI: hopes, controversies and the future of faculty roles in education

Purpose Generative artificial intelligence (GAI) has seen exponential growth in recent years due to its capability to generate original content through natural language processing and comprehensive language models. This paper aims to investigate the transformative impact of GAI on higher education, focusing on the evolving roles of faculty in the classroom. Design/methodology/approach Using a phenomenological perspective and a process approach, the study involved 25 semi-structured interviews with academicians in higher education. Findings The findings reveal that GAI currently creates biased and commercially driven learning environments, challenging traditional pedagogical models. Despite its potential for enhancing education, the autonomous nature of GAI often prioritizes commercial interests over pedagogical goals. Research limitations/implications The study is limited to faculty perspectives, suggesting future research should include student viewpoints and diverse educational contexts. Practical implications The study highlights the need for higher education institutions to develop comprehensive policies, provide training for faculty and students and design new courses that leverage GAI for personalized learning experiences and enhanced faculty research. Originality/value This paper contributes to the emerging literature on GAI’s impact on education, highlighting its dual nature as both a transformative tool and a potential threat to traditional educational roles and outcomes.