Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLO’s multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems.

Cross-modal and immersive technologies offer new opportunities for experiential learning in early childhood, yet few studies examine integrated systems that combine multimedia, mini-games, 3D exploration, virtual reality (VR), and augmented reality (AR) within a unified environment. This article presents the design and implementation of the Solar System Experience (SSE), a cross-modal extended reality (XR) learning suite developed for preschool education and deployable on low-cost hardware. A dual-perspective evaluation captured both preschool teachers’ adoption intentions and preschool learners’ experiential responses. Fifty-four teachers completed an adapted Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) questionnaire, while seventy-two students participated in structured sessions with all SSE components and responded to a 32-item experiential questionnaire. Results show that teachers held positive perceptions of cross-modal XR learning, with Subjective Norm emerging as the strongest predictor of Behavioral Intention. Students reported uniformly high engagement, with AR and the interactive eBook receiving the highest ratings and VR perceived as highly engaging yet accompanied by usability challenges. The findings demonstrate how cross-modal design can support experiential learning in preschool contexts and highlight technological, organizational, and pedagogical factors influencing educator adoption and children’s in situ experience. Implications for designing accessible XR systems for early childhood and directions for future research are discussed.

The rapid diffusion of artificial intelligence (AI) in marketing has reshaped how consumers interact with digital content and evaluate ethical aspects of firms. The present study examines how familiarity with and trust in AI shape consumers’ acceptance of AI-based advertising and, in turn, their ethical purchasing behavior. Data were collected from 505 Greek consumers through an online survey and analyzed using hierarchical and logistic regression models. Reliability and validity tests confirmed the robustness of the measurement instruments. The results show that familiarity with AI technologies significantly enhances trust and ethical confidence toward AI systems. In turn, trust in AI strongly predicts the consumers’ acceptance of AI-driven advertising, while acceptance positively affects ethical consumption intentions. The findings also confirm a mediating relationship, indicating that acceptance of AI-based advertising transmits the effect of AI rust to ethical consumption. By integrating ethical and technological dimensions within a single behavioral model, the study provides a more comprehensive view of how consumers form attitudes toward AI-enabled marketing. Overall, the findings highlight that transparent and responsible AI practices can strengthen brand credibility, foster ethical engagement, and support more sustainable consumer choices.

Following the Itaewon Halloween Crowd Crush of 29 October 2022, this study examines how public sentiment evolved on Naver, South Korea’s most influential digital platform. While prior research has focused on mainstream media and global social networks, little is known about localized discourse on Naver. To address this gap, we analyzed 2107 user-generated posts collected via Python-based web scraping across three time periods: the immediate aftermath, first anniversary, and passage of the Itaewon Special Law. Semantic network analysis, sentiment classification, and logistic regression were applied to uncover patterns in discourse and emotional tone. Results reveal a shift from grief and outrage in 2022 to demands for political accountability, safety reform, and memorialization by 2024. High-frequency keywords reflected media and government narratives, while low-frequency terms exposed grassroots voices and emotional nuance. Regression analysis confirmed statistically significant associations between sentiment, title length, and year. These findings suggest that digital platforms not only mirror public sentiment but also shape the emotional and political framing of national tragedies. By tracing sentiment over time, this study contributes to understanding how echo chambers, narrative framing, and temporal context interact in shaping collective responses to crisis.

This study investigates how Moroccan users experience and interpret digital content that seems tailored to their personal profiles. While many participants recognize the relevance of such content, their willingness to engage depends less on accuracy and more on whether they feel respected and in control. Based on 629 survey responses and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings indicate that perceived control is the most influential factor in building trust, which in turn strongly predicts engagement. Conversely, when content feels intrusive or when users have concerns about how their data is managed, trust declines—even if the targeting appears accurate. These results imply that people do not simply react to what they receive but also to the manner in which it is delivered and explained. In a rapidly digitizing environment like Morocco, where awareness of data rights remains limited, trust and transparency emerge as essential foundations for meaningful digital interaction. The study provides practical insights for marketers and platforms aiming to design targeting strategies that are not only effective but also ethically responsible and aligned with users’ expectations.

This article investigates how Web3 decentralization unfolds in practice and asks two guiding questions: (i) How democratic are decentralized governance systems in practice? (ii) Under what institutional conditions can technological decentralization translate into social inclusion? Based on multi-year ethnographic fieldwork (2022–2025) across Silicon Valley, Washington, D.C., Europe, and the Global South, this study draws on participant observation, semi-structured interviews, and comparative analysis of seven ecosystems—Ethereum, MakerDAO, Uniswap, Mastodon, Celo, Grassroots Economics, and GoodDollar. The findings show that participation asymmetries are structural: token-based governance is dominated by a small group of technically skilled or capital-rich actors, while voter turnout often remains below ten percent. Intermediaries such as foundations, developers, NGOs, and cooperatives are indispensable for coordination, contradicting the idea of hierarchy-free decentralization. In contrast, projects that institutionalize clear membership, monitoring, and accountability—particularly in cooperative and federated settings—display stronger democratic resilience. Comparative evidence also reveals oligarchic consolidation in Global North ecosystems and infrastructural exclusion in the Global South. These results substantiate what Richard R. Nelson termed “the Moon and the Ghetto” paradox: extraordinary technical innovation without corresponding social progress. Interpreted through innovation systems theory, the study concludes that advancing decentralized technologies requires parallel investment in mission-oriented institutions that ensure participation, equity, and accountability in digital infrastructures.

Generative artificial intelligence (AI) is reshaping creative practices, yet many systems rely on traditional interfaces, limiting intuitive and embodied engagement. This study presents a qualitative observational analysis of participant interactions with a real-time generative AI installation designed to co-create Ukiyo-e-style artwork through embodied inputs. The system dynamically interprets physical presence, object manipulation, body poses, and gestures to influence AI-generated visuals displayed on a large public screen. Drawing on systematic video analysis and detailed interaction logs across 13 sessions, the research identifies core modalities of interaction, patterns of co-creation, and user responses. Tangible objects with salient visual features such as color and pattern emerged as the primary, most intuitive input method, while bodily poses and hand gestures served as compositional modifiers. The system’s immediate feedback loop enabled rapid learning and iterative exploration and enhanced the user’s feeling of control. Users engaged in collaborative discovery, turn-taking, and shared authorship, frequently expressing a positive effect. The findings highlight how embodied interaction lowers cognitive barriers, enhances engagement, and supports meaningful human–AI collaboration. This study offers design implications for future creative AI systems, emphasizing accessibility, playful exploration, and cultural resonance, with the potential to democratize artistic expression and foster deeper public engagement with digital cultural heritage.

This study investigates the specific requirements of small and medium-sized enterprises (SMEs) in Southeast Lower Saxony in the context of digital transformation, with a particular focus on aligning these needs with current technological offerings. Utilizing a Technology Radar framework as the methodological approach, the research aims to systematically match identified SME business demands with relevant technological developments, thereby offering a transparent representation of prevailing technology trends. The overarching objective is to support regional SMEs and associated institutions in navigating digitalization challenges by providing recommendations derived from the application of this methodology. To this end, the study outlines the theoretical foundations of digital transformation and explicates the operational principles of the Technology Radar. Subsequently, the digitalization needs of SMEs in key regional industries and contemporary technology trends are analyzed and categorized. These findings are integrated within the Technology Radar framework, facilitating a structured comparison between technological supply and SME organizational demand. The study concludes with a discussion of the results and presents practical implementation strategies to guide regional SME stakeholders in their digital transformation efforts.

Artificial intelligence is becoming a defining force in public governance, yet many institutions still struggle to adopt it in ethical, sustainable, and scalable ways. This article reports on a systematic literature review in line with PRISMA 2020 guidelines, covering 67 peer-reviewed studies published between 2014 and 2024. The review shows that AI can help public institutions work faster and more transparently, but it also reveals several common problems. Many organizations still face fragmented data, weak connections between systems, limited digital tools, a lack of staff skills, and ethical risks such as bias and privacy concerns. To address these problems, the study introduces the AI Integration Capability Model, a framework based on the Technology Acceptance Model, Digital-Era Governance, and Dynamic Capabilities theory. The model highlights four institutional pillars: data access and interoperability, digital infrastructure and redesigned processes, workforce skills and learning capacity, and leadership and management reform. Its relevance was tested through a three-round Delphi study with 15 senior experts from Moroccan public institutions, who agreed on the feasibility and urgency of all four pillars. The findings offer policymakers practical guidance for AI adoption and outline a roadmap for aligning innovation with institutional readiness and public trust.

Multimedia learning effectiveness varies widely across cultural contexts and individual learner characteristics, yet existing educational technologies lack computational frameworks that predict and optimize these interactions. This study introduces the Multimedia Integration Impact Assessment Model (MIIAM), a machine learning framework integrating cognitive style detection, cultural background inference, multimedia complexity optimization, and ensemble prediction into a unified architecture. MIIAM was validated with 493 software engineering students from Zimbabwe and South Africa through the analysis of 4.1 million learning interactions. The framework applied Random Forests for automated cognitive style classification, hierarchical clustering for cultural inference, and a complexity optimization engine for content analysis, while predictive performance was enhanced by an ensemble of Random Forests, XGBoost, and Neural Networks. The results demonstrated that MIIAM achieved 87% prediction accuracy, representing a 14% improvement over demographic-only baselines (p < 0.001). Cross-cultural validation confirmed strong generalization, with only a 2% accuracy drop compared to 11–15% for traditional models, while fairness analysis indicated substantially reduced bias (Statistical Parity Difference = 0.08). Real-time testing confirmed deployment feasibility with an average 156 ms processing time. MIIAM also optimized multimedia content, improving knowledge retention by 15%, reducing cognitive overload by 28%, and increasing completion rates by 22%. These findings establish MIIAM as a robust, culturally responsive framework for adaptive multimedia learning environments.