Digital competency is increasingly vital for students in today’s technology-driven world. Despite efforts to enhance digital skills in education, measuring students’ attitudes toward digital competency remains a challenge, especially in developing contexts like Malaysia. This paper develops and validates the upper secondary students’ attitudes towards digital competency instrument (USSADCI) in Malaysia, providing a psychometrically reliable and valid tool specifically designed while addressing a gap in previous research. Using the Rasch measurement model (RMM), the USSADCI measures constructs such as digital technology application, problem-solving, interpersonal skills, data and information literacy, content creation, digital security, and digital citizenship. A survey method was employed, collecting 47 feedback from students in the urban secondary schools and 43 feedback from the rural secondary schools in the State of Perak. The analysis found that the reliability of the item was 0.96 with a separating index of 4.77. While the respondent’s reliability was 0.92 with a separating index of 3.42. The 14 items were removed due to misfit, resulting in a final 42-item instrument. The study concludes that USSADCI is a robust tool for measuring digital competence attitudes in secondary education. Future research should expand its use to other regions and demographics, exploring the longitudinal impact of digital competence attitudes on students’ academic performance and digital readiness.

From content consumers to content creators: Farmers using TikTok in northern Vietnam's mountainous regions

Will people embrace AI art? Deconstructing psychological barriers in human appraisal of AI-labeled artworks

Advances in digital technolgy require Micro, Small, and Medium Enterprise (UMKM) in Pontianak City to adspt to online marketing strategies to remain competitive, particularly in expanding market reach and increasing sales. This study aims to analyze the use of blogsas an effective, effcient, and low-cost digital promotional medium for UMKM. The method used is mentoring and training in blog content creation for UMKM in Pontianak City. The results show that blogs offer advantages in the form of flexibility in presenting in-depth product information, building brand identity, and increasing visiility in search engines (SEO) compared to conventional social media. Through the use of blogs, UMKM can overcome limited promotional budgets while still reaching a wider audience in a professional manner. This mentoring has been proven to help business actors in digitalizing the marketing of Pontianak’s culnary products.

Abstract In today’s digital era, the rapid growth of deep learning technologies has made it possible to create highly realistic fake media, commonly known as deepfakes. These manipulated images, videos, and audio clips are becoming increasingly difficult to distinguish from genuine content, raising serious concerns about misinformation, privacy, and cybersecurity. While deepfakes have useful applications in areas such as entertainment and content creation, their misuse poses significant risks to individuals and society. The rapid advancement of generative artificial intelligence has enabled the creation of highly realistic synthetic media, commonly known as deepfakes, which pose significant threats to digital trust, security, and personal identity. Detecting these sophisticated forgeries requires robust machine learning models capable of identifying subtle manipulation artifacts across diverse, real-world data distributions. This paper explores the critical challenges in deepfake detection, particularly focusing on the limitations of existing single-stage and globally-averaged detection methods. We propose a comprehensive, hypothetical detection framework that integrates a local-global spatial ensemble approach with multimodal next-frame feature prediction. Furthermore, we outline a multi-faceted evaluation plan incorporating advanced metrics such as Shannon entropy and rough set theory to assess both predictive performance and model robustness. Ultimately, this paper provides a structured analysis of the practical, ethical, and technical dimensions of deploying deepfake detection systems in the wild. This research paper focuses on the use of machine learning techniques to detect deepfake content effectively. It explores different approaches, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models that combine spatial and temporal analysis. The study also examines widely used datasets, preprocessing methods, and evaluation metrics to assess model performance. In addition, the paper highlights key challenges such as the continuous improvement of deepfake generation techniques, limited availability of diverse datasets, and difficulties in real-time detection. Finally, it discusses future directions, including the development of lightweight models and the integration of explainable artificial intelligence for better transparency. Overall, this research aims to provide a clear understanding of current deepfake detection methods and emphasize the need for more robust and adaptive solutions to address this growing threat.

ABSTRACT Artificial intelligence (AI) is reshaping marketing practice and consequently marketing education. While prior pedagogical research has examined traditional AI tasks and more recently generative AI (GenAI) in marketing education, limited attention has been devoted to the emerging paradigm of agentic AI. This requires instructors and students to move from content creation toward system design, process orchestration and execution of marketing tasks using autonomous AI agents. This paper introduces a novel workflow automation assignment that integrates agentic AI into marketing education. We conceptualize agentic AI for marketing education, distinguish it from traditional and generative AI and develop hypotheses regarding its impact on student satisfaction, perceived learning, and engagement in marketing process automation. In this research, a survey was conducted to measure the teaching effectiveness and overall satisfaction of graduate level marketing students (n = 71) in a French university. The evidence suggests that the proposed assignment using n8n platform has positive outcomes in students’ learning experience and engagement.

This paper presents the architecture, design rationale, and empirical evaluation of an AI-Powered System for Dynamic Content Creation, Storage, and Retrieval—a centralized, web-based platform engineered to streamline the complete lifecycle of digital content. The system orchestrates multiple state of-the-art Large Language Models (LLMs), including NVIDIA NIM (Llama 3.1), Groq (Llama 3.1), Cerebras CS-3, and Cohere, within a unified multi-model orchestration layer built on Next.js, TypeScript, and PostgreSQL. Core contributions include: (1) a dynamic AI orchestration layer that routes generation requests across LLM providers based on latency and reasoning requirements; (2) an integrated real-time fact-checking module powered by Google Search APIs to detect and flag AI-generated hallucinations; (3) an automated content quality pipeline delivering readability, uniqueness, and factual-accuracy scores; and (4) a structured semantic knowledge base enabling Retrieval Augmented Generation (RAG) for document-centric workflows. Experimental results confirm that the platform reduces average content-generation cycles from multi-hour manual processes to sub-minute automated workflows, while measurably improving factual accuracy and content quality across academic and professional use cases. The modular architecture is designed for scalability, supporting future extensions including multimodal generation, collaborative editing, and decentralized P2P storage.

The purpose of this article is to examine how university students in Kenya interpret and react to ethnic stereotype messages in the popular comedy Churchill Show, which aired on television for more than a decade. While a lot of studies have been done on the prevalence and proliferation of ethnic stereotypes in the show, little has been done on the audience and the interpretation of the stereotypes. This was a qualitative study using the case study method. It involved holding Focus Group Discussions (FGDs) with 27 students from four universities in Kenya, with diverse backgrounds, aimed at representing the general audience of the show. The participants first watched an episode of Churchill Show, Season 7 Episode 6, which aired on television in October 2017, which set the tone for the discussion. The findings reveal that most of the audience members disapprove of the use of ethnic stereotypes in comedy. Participants expressed concern that negative stereotypes could affect self-esteem for the communities stereotyped. The audience uses a variety of cues and tools to identify and interpret the stereotypes. Most of the students acquired stereotypes and ethnic stereotypes about other communities early on in life from their parents and close social interactions as they were growing up, indicating that comedy mostly plays a reinforcing role. The findings suggest the current generation of young people, commonly called the ‘Generation Z’ or ‘Gen Z’, disapprove of the use of ethnic stereotypes in television content and are moving away from tribe and ethnicity as an identity, and so for them, ethnic stereotypes are no longer relevant. This study recommends the exclusion of ethnic stereotypes in content meant for mass consumption by media houses and other content creators.

The subject of the study is the architecture and functional implementation of a prototype web-oriented intelligent tutoring system for adaptive mathematics learning in an online environment. The purpose of the work is to develop and substantiate the concept of an intelligent web-based system that provides parameterized generation of mathematical problems, automated answer validation, a multi-level hint mechanism, and an individual learning trajectory based on a probabilistic knowledge mastery tracing model. To achieve the goal, the following tasks were addressed: the analysis of modern approaches to building intelligent tutoring systems and knowledge tracing models; the formation of a domain model using a knowledge graph with prerequisite links; the development of modules for template-based problem generation and automated answer validation; the formalization of an adaptive learning scenario based on Bayesian updating of mastery probability; the design of a three-level contextual hint mechanism; the creation of interface solutions for entering mathematical expressions and providing immediate formative feedback; and the evaluation of the potential use of generative artificial intelligence for automating learning content creation. The research methods include systems analysis, functional decomposition, a comparative literature review on intelligent tutoring systems and computer-aided mathematics assessment, the implementation of the classical probabilistic knowledge tracing model, and prototyping of a client-server web system (with an Angular frontend). Conclusions. The developed prototype implements a closed-loop adaptive learning cycle that includes dynamic problem generation, answer input and validation, the provision of contextual hints, the updating of knowledge mastery assessment, and the selection of the next learning step. The system is already operational as a public web prototype and creates a foundation for the empirical evaluation of the effectiveness of adaptive mathematics learning. The scientific novelty lies in the comprehensive integration within a single web platform of: template-based problem generation, automated validation of mathematical expressions, a hierarchical hint mechanism, a probabilistic knowledge tracing model for trajectory management, a knowledge graph with prerequisites, and the justification for the use of generative artificial intelligence in automating content creation and error diagnostics.

The utilization of Artificial Intelligence-Generated Content (AIGC) has attracted widespread attention in video content creation. To generate high-quality videos, this paper presents a controllable multimodal fusion architecture for AIGC-driven short-video production. This architecture employs hierarchical constraint mechanisms and a multimodal attention fusion mechanism to enhance video content coherence and user controllability. Specifically, a scene coherence scheme is first designed to construct graph-based global and transition-level constraints by integrating text descriptions, reference images, and audio features. By leveraging the extracted style vector data, preliminary video clips are then generated through a combination of the cross-modal fusion unit and the spatio-temporal consistency unit. Finally, a fine-grained adjustment mechanism is implemented to ensure logical consistency and stylistic uniformity in the AIGC-generated videos. Experimental results indicate that the proposed architecture improves generation quality, controllability, and cross-segment coherence under the adopted evaluation settings.

Purpose The study responds to address the practical problem faced by the digital marketers, content creators and digital business agencies on creating content, which is both human-readable and LLM-compatible. The present study identifies and analyses the key factors influencing content optimization for Large Language Models (LLMs) to develop a strategic framework for Large Language Model Optimization (LLMO) that aligns with modern search paradigms. Design/methodology/approach This research employs a two-phases multi-criteria decision-making (MCDM) approach combining CRITIC (Criteria Importance Through Intercriteria Correlation) to determine factor weights, and DEMATEL (Decision-Making Trial and Evaluation Laboratory) to map causal relationships. A panel of 15 experts across three countries (India, UAE and USA) rated the influence of five identified factors. Findings The study identifies five critical factors for LLMO: Retrieval Augmentation, Readability Enhancement, Content Quality Assurance, Filtering of Unsafe Content and User-Centric Content Design. Retrieval Augmentation and User-Centric Design emerged as key causal factors, while Readability and Content Quality acted as bridges or effects. Although factor weights were relatively balanced, the DEMATEL analysis revealed interdependencies highlighting the dynamic nature of LLMO. Practical implications The results provide actionable guidance to digital marketing experts and agencies, content strategists, marketing heads and developers to structure web content that is both human-readable and LLM-compatible. The study offers insights to organizations on how they can enhance their digital visibility and authority in AI-powered search ecosystems. Originality/value This study fills a critical gap by offering the first integrated CRITIC-DEMATEL framework for LLMO. It distinguishes LLMO from traditional SEO and offers a novel causal model to support the development of holistic, future-ready content strategies.

This paper critically examines the transformative impact of Generative Artificial Intelligence (GenAI) on public relations (PR) strategies, the evolving roles of practitioners, and the future trajectory of corporate communication. Generative Artificial Intelligence (GenAI) refers to artificial intelligence models capable of producing novel content (text, images, code) based on learned patterns. Its relevance to PR stems from its potential to automate communication tasks, enhance personalization, and provide data-driven insights. This study employs an integrative literature review methodology, synthesizing and critically analyzing existing academic and industry research to develop a conceptual understanding of GenAI's impact on the PR field. The analysis indicates that GenAI significantly alters PR practices, particularly in content creation, research, and personalization, offering unprecedented efficiency and scale. Concurrently, this transformation establishes a clear shift in practitioner skill sets toward strategic oversight, ethical judgment, critical thinking, and prompt engineering. Ethical priorities—including ensuring accuracy, mitigating bias, maintaining transparency, and addressing intellectual property considerations—stand at the core of responsible practice. The future of corporate communication is defined by a hybrid human–AI model in which GenAI actively augments human strategic capabilities rather than replacing them. These findings directly inform PR practice through the imperative of adaptation and continuous upskilling, guide education through necessary curriculum innovation, and advance future research by underscoring the importance of empirical investigations into implementation, ethics, and long-term impacts.

Read-alouds and educational television have well-documented benefits for early childhood literacy learning. Meanwhile, media platforms like YouTube are becoming a growing force in the lives of children. Yet, little is known about the content and educational affordances and constraints of YouTube content design for young learners, such as read-alouds. Drawing on frameworks from emergent digital literacy and multimodality, this study presents a content analysis of the 50 most-viewed YouTube videos found when searching for “preschool read-alouds.” Here, we examine their video features (e.g., reader presence, text/page display, visual/sound effects), story features (e.g., genre, character types, instructional focus), and instructional strategies used before, during, and after the reading (e.g., asking a question, modeling a think aloud, giving a next step). The findings indicate that while many videos used multimodal elements to enhance the story delivery (e.g., sound effects and animated text), few incorporated features known to promote deeper comprehension, such as contextualized questioning, vocabulary support, or interactive prompts. These findings have implications for early childhood educators, caregivers, and content creators, highlighting the need for more intentional design of digital read-alouds that align with early literacy development goals, as well as the important role of adult mediation of YouTube content for children.

Even though generative artificial intelligence (GenAI) is increasingly integrated into user-facing technologies like social media, its impact on content marketing remains unverified. Early evidence suggests that language models (LLMs) can generate content that rivals human-created content (HCC) in terms of appeal. However, the question of adapting such content for various social media platforms remains unanswered. This study examines the effectiveness of an LLM, GPT-4, in customizing cross-platform content for Facebook, Instagram, and X. A total of 892 participants evaluated 30 pairs of AI-created content (ACC) and HCC. The findings reveal that ACC was preferred by users, delivered stronger calls to action, and elicited more user engagement than HCC, especially on Facebook, with a less pronounced effect for shorter posts on X and Instagram. We further generated six data-driven user personas of the 892 participants, illustrating the differences between those who preferred ACC or HCC on the three platforms. The results indicate that GPT-4 can adapt content to platform-specific requirements and maintain high perceived quality, making LLMs applicable for cross-platform content creation for user engagement. Findings contribute to understanding user engagement with AI-generated content across platforms. We also discuss the role of LLMs in content creation, including their ethical implications.

The phenomenon of social media use, particularly TikTok, has attracted attention in various studies, but research that specifically discusses creators’ communication styles in endorsement activities, especially in local contexts such as Padang City, remains limited. This study aims to analyze Curip Almahdi’s communication style in endorsing products on TikTok social media and to understand its influence on audience responses. This study employed a qualitative approach with a case study design, involving a number of informants consisting of the creator, business actors, and audiences selected through purposive sampling. Data were collected through in-depth interviews, observation of TikTok content, and documentation, and were then analyzed using thematic analysis techniques. The results showed that the communication style used was interpersonal, relaxed, open, and expressive, thereby creating emotional closeness and increasing audience engagement. In addition, the dominance of endorsement content, which reached around 70%, accompanied by high audience interaction, indicates that communication style plays an important role in the effectiveness of delivering promotional messages. These findings contribute to the development of communication style theory and interpersonal communication in the context of digital media, as well as broadening understanding of endorsement practices on social media. The conclusion of this study affirms the importance of an authentic and communicative style in enhancing the effectiveness of digital promotion, and implies the need for content creators and business actors to develop communication strategies that are more adaptive and creative. This study also opens opportunities for further research on the relationship between communication style and consumer behavior on social media.

ABSTRACT This article examines Hungary’s digital grassroots feminist media landscape, which emerged in the early 2010s and has since sustained public platforms for women, amplifying their voices against the backdrop of an increasingly hostile illiberal government. Drawing on semi-structured interviews conducted in early 2025 with content creators purposefully sampled from feminist online platforms, the article maps this emerging digital ecosystem, focusing on how these spaces are used by women, and on their impacts (or the lack thereof) on the Hungarian illiberal government (2010-2026). The findings indicate that content creators primarily use these platforms for activities that are not overtly political, such as education, the public articulation of women’s lived experiences, and the accumulation and dissemination of expert knowledge, while only occasionally engaging in conventional political action, consistent with prior research on civil society in repressive political contexts. Interviewees justified this orientation toward ostensibly non-political practices by citing structural constraints on effective feminist action under the illiberal regime. Nevertheless, these practices can be interpreted as constituting a foundational cultural phase that diffuses new values and gradually reshapes public perceptions, thereby contributing to the broader cultural groundwork necessary for subsequent forms of formal political action.

ABSTRACT Platformisation, dominated by Global North Silicon Valley firms, is central to technocapitalism, yet its heteronormative organising logic remains under-theorised in marketing. This omission is particularly acute for queer consumers operating within Global South platform infrastructures. Addressing this gap, this paper theorises queer survival under platform capitalism in China. Despite institutional differences from Western neoliberal models, China's state-led techno-capitalism produces distinct yet equally urgent crises of platformised heteronormativity. This paper asks: how do queer users navigate and rework such power? Drawing on five years of online observation of queer content creators, platform stakeholders, and secondary interviews, I develop the concept of queer survivalscapes: gong sheng (symbiotic), ji sheng (parasitic), and ye sheng (feral). I theorise queer consumers as precarious yet strategic actors who redistribute risk through relational, performative, and affective labour, advancing technology and queer consumer studies beyond Western-centric accounts of platform power.

Social media platforms such as Instagram, TikTok, and OnlyFans have created a parallel economy in which individuals can monetize creativity, personality, and visibility at unprecedented speed. This emerging “creator economy” challenges the traditional higher-education model by offering an alternative to the degree-to-career pipeline. The prospect of fast financial returns and personal autonomy has enticed many young adults to prioritize content creation over formal education. This paper explores how the rise of digital monetization reshapes perceptions of success, value, and educational relevance. Finally, the paper will offer evidence-based recommendations for re-engaging students in the classroom by integrating digital-creator literacy, hybrid credentialing, and flexible learning models to attract students back to the classroom.

This phenomenological study investigated how Department of Education (DepEd) teachers in the Philippines experience and interpret their engagement in social media content creation alongside formal teaching responsibilities. In a context characterized by high digital participation and expanding creator economies, the study specifically analyzed motivations, identity construction, boundary-setting practices, monetization decisions, and instructional adaptations shaped by sustained online engagement. Five purposively selected public-school teachers from the elementary and secondary levels, each with a minimum of six months of active content creation experience, participated in in-depth, semi-structured interviews. Data were transcribed verbatim and analyzed using a systematic phenomenological procedure guided by Creswell’s six-step framework. Trustworthiness was established through credibility checks, audit trails, reflexive bracketing, and thematic validation. The study identifies eight main themes: (1) Passion Meets Platform: The Spark Behind the Screen; (2) Balancing Acts: Navigating Dual Roles with Grit; (3) Beyond the Classroom: Growth, Gains, and Gratitude; (4) Redefining the Teacher Identity: Empowerment Through Expression; (5) Ethics and Boundaries: Navigating Professionalism in the Digital Space; (6) From Stress to Self-Care: Content Creation as Emotional Outlet; (7) Monetization as Motivation: The Financial Frontier of Teaching; and (8) Inspiring by Example: Teachers as Digital Role Models. Results indicate that participants deliberately engineered their digital identities, applied platform analytics to refine communication strategies, and constructed public personas aligned with institutional expectations and audience demands. The most significant realization was that teachers can and should systematically study how content creators navigate digital infrastructures, shape public identities, and strategically deploy online tools for the benefit of their students. The study demonstrates that teacher influencers function as pedagogical practices beyond the classroom, a strategy for income generation, and an intentional redefinition of professional roles under conditions of constant online visibility and audience feedback.

Clickbait or Truth? Analyzing the Relationship between misinformation and engagement on TikTok regarding epilepsy treatment.