It’s Not Porn, It’s Sexual Abuse: A Scoping Review of Sexual Deepfakes Public Opinions, Perpetration, and Harms

All through the past few years, digital platforms have witnessed a widespread and a significant increase in their popularity, coinciding with the increasing dependence on modern technologies and the increasing need for communication and distance education. Digital platforms have proven their efficiency in various educational fields, but few studies have addressed the role of digital platforms in improving the study of the Qur’an. The Prophet’s Mosque is one of the biggest mosques that provides the service of studying the Holy Qur’an relying on multiple methods, including digital platforms which are used in a specific context for teaching the Qur’an to students from various cultural backgrounds from different countries. Accordingly, the current study aims to study the case of digital platforms used in teaching the Holy Qur’an in the Prophet’s Mosque by relying on listening to (7) voices of students studying the Qur’an from different cultural backgrounds. To implement this study, the qualitative case study approach was adapted through interviews and qualitative observation of participating students. According to the outputs of the objective analysis, the results of the study showed that the roles of digital platforms in teaching the Holy Qur’an to students from diverse cultural backgrounds are based on four main topics: educational flexibility, educational efficiency, educational satisfaction, interaction, and participation. These results showed the possibility of expanding the generalization of the experience of the Prophet's Mosque in teaching the Holy Qur’an via digital platforms, as this experience showed that teaching in this way contributes to creating positive attitudes towards digital platforms, enhancing participation in Qur’anic circles, facilitating communication between teachers and students, providing assistance, ease of access, and flexibility of study, which contributes to the continuation of education.

Generative Artificial Intelligence (GAI) represents a significant shift in the way people interact with technology, with a growing impact in sectors such as education, communication, and content creation. Although its applications promise relevant benefits for people with cognitive disabilities, such as language simplification or support for task organization, there are also significant accessibility challenges that remain unexplored. This paper presents the results of a systematic literature review focused on the cognitive accessibility of textual GAI interfaces. The main objective was to identify the current cognitive accessibility challenges inherent in textual GAI interfaces and to synthesize the solutions proposed to promote a more inclusive interaction. To this end, seven studies selected between 2020 and 2024 from four academic databases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library) were analyzed. The analysis reveals that current interfaces impose excessive cognitive load and suffer from systemic lack of predictability and user scaffolding due to their reliance on complex prompt engineering and low explainability. Emerging proposals based on inclusive design, personalization, multimodal interfaces, and ethical frameworks are also identified. The paper concludes by highlighting the need for more empirical studies, specific design guidelines, and validated evaluation methods to ensure that generative technologies are truly accessible to all.

The emergence of Artificial Intelligence Generated Content (AIGC) represents a new opportunity for the development of intelligent communication. The development of AIGC technology has given rise to new media transformations, creating new content production and dissemination methods. As one of the core areas of the AIGC application, generative speech technology, due to its low cost and simplicity, has been widely used in scenarios such as audiovisual narration, artificial intelligence anchor broadcasting, and audiobook content production. However, the use of AIGC-generated content in practice, due to default program settings and low barriers to entry, leads to the proliferation of homogenization. On the regulatory side, the lack of effective oversight has led to the misuse of technology and the emergence of communication risks and ethical issues in artificial intelligence. Lastly, the decentralized and fragmented communication model has led to the uncontrollable emergence and development of public opinion events, diluting the discourse power of authoritative media, with rumor clarification becoming the main post-event means to clarify public opinion whirlpools. Based on this, this paper starts from the application scenarios of generative speech technology, reflects on the new issues in the field of "artificial intelligence + speech generation" applications, and aims to provide a deeper technological reflection and new development ideas.

This research delves into the impact of Chat Generative Pre-trained Transformer, one of Open Artificial Intelligence Generative Pretrained Transformer models. This model underwent extensive training on a vast corpus of internet text to gain insights into the mechanics of human language and its role in forming phrases, sentences, and paragraphs. The urgency of this inquiry arises from Chat Generative Pre-trained Transformer emergence, which has stirred significant debate and captured widespread attention in both research and educational circles. Since its debut in November 2022, Chat Generative Pre-trained Transformer has demonstrated substantial potential across numerous domains. However, concerns voiced on Twitter have centered on potential negative consequences, such as increasedforgery and misinformation. Consequently, understanding public sentiment toward Chat Generative Pre-trained Transformer technology through sentiment analysis has become crucial. The research’s primary objective is to conduct Sentiment Analysis Classification of Chat Generative Pre-trained Transformer regarding public opinions on Twitter in Indonesia. This goal involves quantifying and categorizing public sentiment from Twitter’s vast data pool into three clusters: positive, negative, or neutral. In the data clustering stage, the Self-Organizing Map technique is used. After the text data has been weighted and clustered, the next step involves using the classification technique with LongShort-Term Memory to determine the public sentiment outcomes resulting from the presence of Chat Generative Pre-trained Transformer technology. Rigorous testing has demonstrated the robust performance of the model, with optimal parameters: relu activation function, som size of 5, num epoch som and num epoch lstm both at 128, yielding an impressive 95.07% accuracy rate.

BackgroundPeople experiencing homelessness have worse oral health outcomes and a notable health informational asymmetry compared to the general population. Screening programs present a viable option for this population; however, barriers to access, such as lower levels of health literacy, lack of information, and mistrust, narrow their chances to participate in such programs.ObjectiveThe aim of this study is to investigate the applicability of generative artificial intelligence (AI) in designing a homeless health screening program fact sheet with experts by experience using co-design principles.MethodsSix fact sheet text variants were created by the open-access version of ChatGPT 3.5 for an oral cancer screening program targeting people experiencing homelessness in Budapest, Hungary. Clients of homeless social services (N=23) were invited to a short questionnaire survey and 3 semistructured focus group discussions between May and July 2024. General opinions regarding generative AI technology and direct feedback on the text variants were obtained. Additionally, a standardized readability assessment of the text variants was completed via the Sydney Health Literacy Lab Editor.ResultsAlmost two-thirds of participants (17/23) stated that they had previously heard about AI; however, their self-assessment regarding the extent of their knowledge resulted in an average of 2.38 (n=16) on a 5-point Likert scale. During the first focus group discussion, all 6 variants received a high score (between 4.63 and 4.92 on a 5-point Likert scale). In the next sessions, participants remained positive when the pool was narrowed to 4 versions, although they scored the texts lower. During open discussions, text variants were considered understandable, while difficulties with medical expressions, lengthiness of sentences, and references to a stereotypical homeless subgroup (rough sleepers) were also reported. The health literacy editor showed that most AI-generated text variants were difficult to read and too complex for the target group.ConclusionsThe co-design process revealed that focus group participants actively wanted to shape the fact sheet drafts. They shared their insights on how to make the text variants more appealing for the target audience. Moreover, the involvement of generative AI technology revealed that the participants have heard about the concept of AI and text generation as a potential function, and they have not rejected its use in health care settings.

Childhood physical and sexual abuse are known risk factors for adult sexual aggression perpetration and victimization, but less is known about the role played by childhood emotional abuse. College sophomores were surveyed regarding their childhood physical, sexual, and emotional abuse victimization and their late-adolescent experiences of sexual aggression victimization and perpetration. Controlling for social desirability and childhood physical and sexual abuse, childhood emotional abuse was the strongest predictor of adolescent sexual perpetration for women and the strongest predictor of adolescent sexual victimization for men. Emotional abuse was a marginally reliable predictor of adolescent sexual victimization in women. These results show the importance of childhood emotional abuse victimization as a risk factor for sexual victimization and perpetration in adolescent intimate relationships.

<p><strong><em>The purpose of the study –</em></strong><em> analysis of Russia's information campaign in the occupied Crimea during 2014–2022.</em></p><p><strong><em>Research methods. </em></strong><em>A combined approach was used, including qualitative and quantitative methods. The main method of the research was the content analysis of mass media, which made it possible to identify the key messages and narratives that were used to influence public opinion. Also, an important component of the analysis was the cross-verification of information received from various sources, in particular, from independent media and human rights organizations, which made it possible to assess the objectivity and credibility of propaganda messages. Considerable attention was paid to the analysis of digital content, including social media and Internet resources, which have become the main channels of information dissemination and manipulation.</em></p><p><strong><em>Results.</em></strong><em> The strategies and methods of Russian information campaigns in Crimea were investigated, the key stages of the change in control over the media sphere were identified, which affected public opinion and the media space of the region. It is shown that these campaigns were aimed at both domestic and international audiences in order to legitimize the occupation and create an image of Russia as a protector. The use of disinformation, propaganda and repression against media workers has been established, which contributed to the transfer of Ukrainian media under the control of Russia, the reduction of media diversity and the maintenance of a one-way information flow. The role of digital platforms and social networks in supporting media pluralism and independent journalism despite control and repression is assessed.</em></p><p><strong><em>Scientific novelty. </em></strong><em>The study for the first time systematically highlights the periodization of strategies of information manipulation in Crimea and reveals the key methods influencing the formation of public opinion in the occupied region.</em></p><p><strong><em>Practical significance.</em></strong><em> The results of the study can be used for the development of media literacy and information security strategies necessary to counter information aggression. They also contribute to a better understanding of the tactics of psychological influence on the public, which is relevant for governmental and non-profit organizations concerned with issues of national security and sovereignty.</em></p><p><strong><em>Key words: </em></strong><em>information and media space, information war, mass media, propaganda, narrative, manipulation.</em></p>

[Objective] This study aims to conduct an in-depth analysis of the multifaceted impact of Generative AI technology on educational settings, with a particular focus on the changing roles of teachers, and to systematically explore effective strategies for utilizing AI in educational environments. [Contents] This study employed a systematic literature review methodology to investigate domestic research trends on generative AI in education. Data was collected from online databases (RISS, KISS, etc.) for publications up to January 2025, with literature selection and analysis conducted according to PRISMA guidelines [Conclusions] Based on the results of the study, Generative AI holds various educational values, including creating customized learning content, increasing teacher work efficiency, supporting creative learning activities, learning analysis, and providing personalized feedback. However, concerns were also raised regarding ethical issues like data bias, copyright infringement, and personal information protection, as well as the potential reduction in the role of teachers. This study emphasizes the need for teacher competency enhancement, the establishment of ethical guidelines, student-centered educational design, securing the public nature of educational systems, and continuous research and evaluation for the successful integration of Generative AI technology into education.

The paper examines the programming and organisational responses of FM radio to a dual crisis situation in the Republic of Croatia. At the very beginning of the national COVID-19 lockdown, on 22 March 2020, the capital city of Zagreb was struck by the strongest earthquake in the last 140 years. With the purpose of ‘recording’ the situation at the time when the disaster was actually occurring, the research aims to question how the radio stations in the area acted under such unprecedented crisis circumstances. Methodologically, it is a qualitative and descriptive research based on the analysis of questionnaire answers. Open-ended questions are grouped into three main topics: organization and programme changes, sources of information and the role of digital platforms. The results reveal that radio stations tended to retain their formatted programme content regardless of the dual crisis. This finding indicates the necessity to re-examine the overall role of FM radio in a modern democratic society.

Objective. The rapid integration of Generative Artificial Intelligence (AI), especially tools like ChatGPT, into educational sectors has spurred significant academic interest. This review article provides a systematic examination of the current scholarly landscape concerning the use of ChatGPT within higher education. Design/Methodology/Approach. Drawing from a range of academic databases between 2022 and 2024, we meticulously adhere to PRISMA guidelines, evaluating a final set of 28 out of 1740 initial articles based on predetermined inclusion and exclusion criteria. Results/Discussion. Our analysis reveals diverse global contributions predominantly from Asia and identifies a prevalent quantitative research approach among the studies. We delve into the selected articles' geographical distribution, methodologies, and thematic outcomes, highlighting a notable lack of research from Latin America. The review critically assesses the validity, utility, and time optimization aspects of ChatGPT in educational settings, uncovering a positive impact on student learning and time management. However, we pinpoint a significant gap in rigorous experimental research, underscoring the need for studies with random sampling and controlled settings to enhance the external validity of findings. Additionally, we call attention to the ethical considerations and the necessity for higher education institutions to adapt teaching methodologies to incorporate AI effectively. Conclusion. The article concludes with recommendations for future research to address the identified gaps and optimize the educational use of generative AI technologies like ChatGPT.

Child and adolescent victims of sexual crimes are at high risk for further abuse, which translates in a relatively higher risk of revictimization for a child or adolescent who has already been a victim of sexual abuse, compared to one who has not. Although sexual revictimization has been extensively studied in young and adult populations, much less is known about occurrences in childhood and adolescence. In this vein, this article seeks to identify risk of, and protective factors against, revictimization in a census sample of victims under 18 years of age in Chile. Risk and protective factors were studied in a database of all complaints of sexual offenses against children and adolescents registered at two time-points over two years: first, during 2012, and again through January 2015. Using multiple logistic regression, we identified risk factors associated with the probability of sexual revictimization, such as sex of the perpetrator (OR = 3.6 for female perpetrators), previous sexual offenses attributed to perpetrators (OR = 1.8), age of victim at the time of abuse (6–12 OR = .77; 13–17 = OR = .69), and minimization of abuse (OR = 1.3). Finally, interaction tests identified an association between the sex of the original perpetrator and the sex of the victim, as well as an association between the sex of the original perpetrator and the age of the victim. The implications of these findings for victim protection and victimological public policy are discussed.

In this digital era, Artificial Intelligence (AI) has emerged as a profound and innovative tool across various aspects of human life—including entertainment, economy, education, and increasingly, the political sphere. The rapid development of AI in recent years has shown significant impact, including its involvement in political party decision-making processes. This study explores the political relevance of AI utilization within the realm of political communication, focusing on the analysis of its role and significance in the dynamics of Indonesia’s 2024 general election campaign. The aim is to examine AI’s potential in the political landscape by identifying the types of AI employed, both as supporting tools and in other assistive capacities. Through a qualitative approach, this research investigates the impact of AI on modernizing political campaign strategies, enhancing the efficiency of political communication, and influencing decision-making processes within political contexts. The methodology includes content analysis of campaign materials involving AI technologies and observation of political interactions mediated by artificial intelligence. Accordingly, this study seeks to provide an in-depth understanding of how the utilization of AI in political campaigns can shape political dynamics, public opinion, and democratic processes in Indonesia. The findings reveal that AI has been employed not only as a tool for generating visual content and political narratives but also as a strategic communication medium to shape voter perception and increase public engagement. Generative AI technologies, such as image generators, have demonstrated emotional appeal in political campaigns, as evidenced in the campaigns of Prabowo-Gibran and Andra-Dimyati. These findings indicate that AI contributes significantly to the transformation of modern political communication, influencing both public opinion and the democratic process in Indonesia.

This study investigates Indonesian teachers’ perceptions of Duolingo as a supplementary tool for English instruction, focusing on its benefits, challenges, and feasibility in formal education. Using a qualitative case study at SMP Miftahul Husna, data were collected through interviews, observations, and document analysis. Findings indicate that while Duolingo enhances student engagement and autonomous learning through gamification, challenges persist regarding its limited emphasis on productive language skills, curriculum alignment, and technological accessibility. Teachers highlight the need for a blended learning approach that integrates Duolingo with structured classroom instruction to support communicative competence. This study offers insights into the role of digital platforms in Indonesian education and provides recommendations for their effective integration.

As generative AI technologies like ChatGPT become increasingly integrated into various aspects of daily life, understanding public perception is crucial for guiding responsible development and ethical deployment. This study conducts a comprehensive sentiment analysis of Twitter discourse, utilizing an innovative approach that integrates Plutchik’s Wheel of Emotions, the NRC Word-Emotion Association Lexicon, and the VADER algorithm. By analyzing a dataset of 39,051 tweets, the research aims to identify predominant emotions, sentiment intensity and distribution (positive, negative, and neutral), and underlying themes within the discourse. The findings reveal that trust, anticipation, and joy are the most frequently expressed emotions, reflecting a generally positive reception of ChatGPT. Specifically, 54.4% of the tweets conveyed positive sentiments, 17.02% were negative, and 28.58% were neutral. Thematic analysis, facilitated by Latent Dirichlet Allocation (LDA) and Gibbs Sampling, uncovers key themes related to ChatGPT’s potential, functionality, and utility. This study contributes to a deeper understanding of public attitudes towards generative AI technologies, providing valuable insights for developers, policymakers, and researchers in addressing the ethical, practical, and societal implications of AI integration into everyday life.

Limiting global warming to 1.5 °C requires negative emission technologies (NETs), which remove carbon dioxide from the atmosphere and permanently store it to offset unavoidable emissions. Successful large-scale deployment of NETs depends not only on technical, biophysical, ecological, and economic factors, but also on public perception and acceptance. However, previous studies on this topic have been scarce. In 2019, Switzerland adopted a net zero greenhouse gas emissions by 2050 target, which will require the use of NETs. To examine the current Swiss public perception and acceptance of five different NETs, we conducted an online survey with Swiss citizens (N = 693). By using a between-subjects design, we investigated differences in public opinion, perception, and acceptance across three of the most used frames in the scientific literature — technological fix, moral hazard, and climate emergency. Results showed that the public perception and acceptance of NETs does not differ between the frames. The technological fix frame best reflected participants’ opinion, whereas participants perceived the moral hazard frame the least credible and the climate emergency frame the most unclear. Moreover, our findings confirm the public’s unfamiliarity with NETs. We found no strong opposition, as participants indicated a moderate acceptance and a neutral evaluation of all five NETs, with afforestation standing out as the most accepted and positively evaluated NET. We conclude that, in the future, the public debate on NETs should be intensified, and the public perception should be monitored regularly to inform the development of NETs.