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This analysis identifies the problem of the media saturation of people’s lives as a reason for developing programs teaching media literacy. It argues that the basic or foundational disciplines found in cultural studies, such as applied semiotics, psychoanalytic theory, sociological theory, and Marxist analysis, are the proper way to teach media criticism and media literacy (See Berger, A.A., Media Analysis Techniques, 6th edition, 2019). The methods by themselves are not adequate, which means that teaching media literacy also involves providing exercises and learning games that show students how to apply the theories they learn to their analyses of media texts (See Berger, A.A., Games and Activities for Media, Communication, and Cultural Studies Students,2004). Finally, it is suggested that media literacy should be taught at all educational levels. 

This study investigates problematic smartphone attachment under conditions of short-term smartphone separation. Two experimental studies with randomized group allocation were designed to investigate effects of smartphone separation on anxiety and inhibitory control. Problematic smartphone use pathways were explored using a self-report measure. In the first experiment (N= 85) smartphone addicted participants showed an increase in state anxiety after 20 min of separation from their smartphones compared to a control group of non-addicted participants. There was no evidence for impaired inhibitory control based on a period of smartphone separation. In the second experiment the methodology was slightly varied, and the participants (N= 95) were provided with a task during a smartphone separation of 15 min. This led to a reduction of state anxiety for problematic attached participants but did not result in a change for unproblematic attached participants. Problematic attached participants showed a larger disturbance in inhibitory control undergoing a separation period than unproblematic attached participants. Moreover, the results provide supplementary evidence for the existence of specific problematic smartphone attachment pathways and further variables.

Cyberbullying is a form of bullying meted out online using digital devices and platforms. This study investigated the types of cyberbullying undergraduate students in Kenyan universities experience. Specifically, it sought to determine the students’ presence on Facebook, establish how the students used Facebook, identify the types of cyberbullying they experienced, and recommend strategies of mitigating cyberbullying as a means of making Facebook safer for undergraduate students in Kenya. The study was conducted using mixed research methods. Data was collected from 4770 undergraduate students and 24 deans of students in 16 public and 8 private chartered universities in Kenya. Quantitative data was collected from the students’ using questionnaires while qualitative data was collected from students using focus group discussions and deans of students using key informant interviews. Quantitative data was analysed statistically using STATA while qualitative data was analysed thematically using ATLAS.ti. The findings of the study confirm the popularity of Facebook among undergraduate students in Kenya. Students use the platform mainly for dating, business, academics and politics. The main types of cyberbullying experienced by the students on Facebook include shaming, impersonation, blackmail, exposure and cyberstalking. In dealing with cyberbullying, university authorities should create awareness about the vice among their students; the affected students should be counselled to overcome the effects of cyberbullying; and students should be advised to be vigilant in cyberspace. The findings of this study may be applied by universities to develop a students’ support service model that makes it easy to detect and mitigate cyberbullying.

After a brief description of what fake news is and how it has been developed historically, we have claimed that fake news as we know it is generally a social media phenomenon. Despite the fact that Iran is considered a developing country, and not still a developed one, one might think Iran is less likely to fall prey of complicated fake news industry that negatively affect prosperous and affluent countries. But the vice versa is correct. Doing an extensive survey on previous research on fake news in Iran, we have shown how fake news will hinder unprecedented facilities for freedom of expression and even will make people contemplate giving up their freedom of expression in order to avoid or get rid of fake news. A naïve view could see a bright future for unbiased and disinterested news gathering and publishing, but research has shown in a totally unregulated media sphere, it is fake news that is more radially available and more quickly becoming viral.

This study presents a structured literature review on the application of AI in credit risk assessment, synthesizing empirical and conceptual research published between 2016 and 2022. It critically examines a range of AI models, including artificial neural networks (ANN), support vector machines (SVM), fuzzy logic systems, and hybrid architectures, with an emphasis on their predictive accuracy, robustness, and operational applicability. The review highlights that AI-based models consistently outperform traditional statistical techniques in handling nonlinear patterns, imbalanced datasets, and complex borrower profiles. Furthermore, AI enhances the inclusivity of credit evaluation by integrating alternative data sources and adapting to dynamic financial environments. However, the study also identifies ongoing challenges related to model interpretability, fairness, and regulatory compliance. By evaluating model performance metrics and methodological innovations across multiple contexts—including emerging markets, peer-to-peer platforms, and digital banking—the study offers a nuanced understanding of AI's strengths and limitations. The paper concludes with a call for balanced integration of explainable AI tools and ethical governance to ensure responsible deployment in financial institutions.

This study presents an integrative review of scholarly literature published between 2016 and 2022 to synthesize how AI literacy has been conceptualized, taught, and measured. Drawing on over 40 empirical and theoretical sources, the review maps trends across educational levels—from primary to higher and adult education—and user groups, including students, teachers, professionals, and the general public. Key findings reveal significant diversity in pedagogical strategies, ranging from project-based learning and digital storytelling to flipped classrooms and professional development programs. Conceptualizations of AI literacy vary widely, encompassing technical, ethical, civic, and empowerment-oriented dimensions. Meanwhile, the field continues to face challenges related to definitional ambiguity, limited standardization of assessment tools, and inconsistent integration of ethical and socio-cultural considerations. The review emphasizes the need for convergence around shared frameworks, scalable pedagogies, and cross-contextual evaluation tools. It concludes by identifying future directions for inclusive, empirically grounded, and ethically aware AI literacy initiatives aimed at fostering reflective and informed engagement with AI systems.