Advanced Journal of Management, Humanity and Social Science

Abstract The proliferation of Artificial Intelligence (AI) companions—conversational agents designed to provide social interaction and emotional support—has prompted significant scholarly debate regarding their psychological and social consequences, particularly for young adults. This study investigates the impact of AI companion use on loneliness, emotional attachment to AI, and interpersonal communication competence among university students (N = 1,200). Utilizing a cross-sectional survey design, we measured social-interaction burnout, subjective loneliness, AI parasocial interaction, emotional attachment to AI, and interpersonal communication skills. Our findings reveal a complex, paradoxical relationship: while AI companions offer immediate relief from loneliness (β = 0.254, p < .001) and are perceived as low-risk sources of social connection, they also significantly predict emotional dependence (β = 0.318, p < .001) which, in turn, negatively correlates with interpersonal communication competence (β = -0.41, p < .001). Furthermore, social-interaction burnout emerged as a strong predictor of AI attachment (β = 0.238, p < .001). The results support the "Algorithmic Sanctuary" hypothesis, suggesting that young adults may retreat to AI interactions to escape the perceived judgment and demands of human relationships, a process that paradoxically may impair the very social skills needed to forge meaningful human connections. This study underscores the need for digital literacy interventions that address the developmental and relational implications of sustained AI companionship.

Abstract The rapid integration of artificial intelligence (AI) systems into core societal institutions—from criminal justice and welfare administration to employment and border governance—has generated unprecedented challenges for international human rights law. This article examines three intersecting dimensions of the AI-human rights nexus: the contested question of AI legal personhood, the allocation of responsibility for AI-induced harms across complex value chains, and the evolving architecture of global AI governance. Drawing on the Council of Europe's Framework Convention on Artificial Intelligence (2024), the UN Guiding Principles on Business and Human Rights as applied to AI (2025), and emerging regulatory frameworks including the EU AI Act, this analysis argues that granting legal personhood to AI systems is neither necessary nor desirable for effective accountability. Instead, a functional approach that mandates human rights due diligence throughout the AI lifecycle, establishes accessible remedy mechanisms for affected individuals, and promotes regulatory coherence across jurisdictions offers a more promising pathway. The article synthesises findings from a doctrinal analysis of 45 international legal instruments, UN reports, and scholarly sources to propose a rights-based governance framework centred on mandatory human rights impact assessments, independent oversight, and meaningful stakeholder engagement with affected communities.

Abstract In 2026, the integration of Artificial Intelligence (AI) into Urban Security Management in Iran has emerged as a strategic and transformative technology, playing a pivotal role in enhancing cyber surveillance, threat prediction, critical infrastructure protection, and urban resilience. This trend, embedded within the National Artificial Intelligence Document of the Islamic Republic of Iran (approved by the Islamic Consultative Assembly in 2025/1404) and the agendas of the Supreme Council of the Cultural Revolution, is observable in major municipalities such as Tehran (through intelligent systems for detecting violations of hijab regulations and traffic monitoring) and smaller cities like Nushabad (focusing on safeguarding cultural heritage and the underground city of Ouyi against cyber and physical threats). However, this integration has engendered profound legal and jurisprudential challenges, primarily stemming from tensions between the security efficacy of AI and fundamental principles of civil rights and Maqāṣid al-Sharīʿah. Recent jurisprudential scholarship (e.g., 2025-2026 analyses of AI ethics in Imami jurisprudence) emphasizes that AI must not become an instrument for violating individual rights; rather, it should be subject to oversight by religious scholars and jurisprudential-legal committees to ensure conformity with Islamic values (such as the principles of lā ḍarar wa lā ḍirār). In this context, proposed models, such as integrating Maqāṣid into the NIST RMF or adopting Islamic ethical frameworks (as implemented in Islamic countries like the UAE and Malaysia), may offer viable pathways. The paper concludes by recommending that Iranian municipalities adopt a hybrid governance approach integrating global technical standards with jurisprudential principles to mitigate AI risks and progress toward sustainable and ethical development. This research is valuable for policymakers, urban security managers, jurists, and legal scholars, and may serve as a foundation for future articles in ISI/Scopus journals on AI Ethics, Islamic Jurisprudence, and Urban Governance.

Abstract Background: The rapid integration of artificial intelligence (AI) technologies in higher education has introduced unprecedented ethical challenges. Universities increasingly rely on AI for administrative decisions, student assessment, research support, and learning analytics. However, these applications raise concerns regarding fairness, transparency, privacy, and potential cultural biases. This study explores the ethical challenges of AI in universities with particular attention to cultural sensitivities and regional norms.

Methods: A mixed-methods approach was employed. Quantitative data were collected via an online survey distributed to 350 faculty members and administrative staff across five universities in culturally diverse regions. The survey measured perceptions of AI ethics, awareness of cultural considerations, and institutional policies. Qualitative data were gathered through semi-structured interviews with 20 stakeholders to explore experiences and perceptions of AI-related ethical dilemmas. Descriptive statistics, thematic analysis, and cross-tabulations were used to analyze the data.

Results: Survey results indicated that 68% of participants were concerned about potential bias in AI algorithms affecting student evaluations. Privacy concerns were reported by 74% of respondents, particularly regarding learning analytics platforms. Cultural sensitivity emerged as a significant issue, with 61% noting that AI tools often fail to account for regional social norms and values. Interview data revealed recurring ethical themes: algorithmic bias, lack of transparency, data misuse, and limited institutional guidelines addressing cultural factors. A sample table summarizing survey responses highlights key ethical challenges.

Conclusion: Universities face complex ethical dilemmas when implementing AI technologies, exacerbated by cultural sensitivities. Developing clear guidelines, culturally-aware AI frameworks, and institutional oversight mechanisms is crucial. Future research should focus on adaptive AI policies that integrate ethical, social, and cultural considerations to promote equitable and responsible AI adoption in higher education.

Abstract Introduction / Background: In the current digital era, organizations face increasingly sophisticated cyber threats that challenge conventional security mechanisms. The growing complexity and volume of cyber incidents demand innovative solutions capable of supporting rapid, accurate, and proactive decision-making.

Purpose / Objective: This study investigates the impact of Artificial Intelligence (AI)-based Decision Support Systems (DSS) on organizational cybersecurity and risk management. Specifically, it aims to examine how AI-driven DSS enhances threat detection, improves incident response, strengthens risk management, and contributes to organizational resilience. Additionally, the study explores challenges, limitations, and governance requirements associated with integrating AI-based DSS into organizational practices.

Methodology: A comprehensive literature review and conceptual analysis were conducted, drawing insights from recent empirical studies, frameworks, and case studies on AI applications in cybersecurity and risk management. The study identifies key constructs, including AI-DSS capabilities, cybersecurity effectiveness, risk management efficiency, governance mechanisms, and organizational readiness, and proposes a conceptual framework linking these constructs.

Results: Findings indicate that AI-based DSS significantly improves cybersecurity performance, enabling faster detection of threats, proactive mitigation, and reduced operational and strategic risk exposure. Furthermore, AI integration supports more informed and timely decision-making, enhancing overall organizational resilience. However, the effectiveness of AI-based DSS is influenced by data quality, human oversight, governance frameworks, and organizational readiness. Potential risks include adversarial attacks, model biases, and over-reliance on automated decision-making.

Conclusion: AI-based Decision Support Systems represent a strategic tool for strengthening cybersecurity and risk management in modern organizations. Successful implementation requires a holistic approach encompassing governance, human-in-the-loop oversight, infrastructure readiness, and continuous evaluation. By integrating AI responsibly, organizations can achieve enhanced security, better risk mitigation, and improved operational resilience.

Abstract In today’s competitive landscape, where brands strive not only for visibility but also for emotional resonance, storytelling has emerged as a strategic pillar of experiential marketing. This study examines the role of artificial intelligence (AI) in advancing personalized brand storytelling and fostering emotional engagement, focusing on L’Oréal’s integration of ModiFace and SkinConsult AI as a case study. Grounded in theories of narrative transportation and experiential branding, the research proposes a conceptual framework that connects AI technologies with individualized storytelling, emotional immersion, and consumer response.

Using a qualitative, exploratory case study approach, the study analyzes secondary data drawn from L’Oréal’s digital campaigns, product platforms, and consumer feedback. A thematic content analysis demonstrates that AI operates not merely as a data-processing tool but as a narrative engine that enables brands to co-create meaning with consumers. Through ModiFace and SkinConsult AI, personalized product experiences are transformed into emotionally resonant narratives, positioning users as protagonists in their own beauty journeys.

Findings indicate that AI-driven storytelling enhances consumer engagement, strengthens brand trust, and fosters identity alignment, ultimately driving loyalty and word-of-mouth advocacy. Furthermore, the case illustrates how AI-enabled personalization contributes to sustainable marketing practices by minimizing product waste through virtual try-ons, broadening access to beauty experiences, and supporting profitable yet responsible growth.

This research enriches marketing scholarship by reframing AI as a storytelling partner and introducing a model that integrates emotion, experience, technology, and sustainability within contemporary brand communication.

Abstract Generative Artificial Intelligence (Generative AI) has rapidly emerged as a transformative force within media organizations, reshaping how content is created, managed, distributed, and monetized. This article examines the applications of Generative AI in three critical domains of media management: content management, creative processes, and intellectual property (IP) governance. In content management, Generative AI enables automation of tasks such as content tagging, summarization, localization, and personalization, significantly improving efficiency and scalability while reducing operational costs. Algorithms capable of generating metadata and optimizing content workflows allow media organizations to respond more rapidly to audience demands across digital platforms. From a creativity perspective, Generative AI functions as a collaborative tool rather than a replacement for human creators. Technologies such as large language models, image generators, and audio synthesis systems support journalists, editors, and designers by assisting in idea generation, drafting, visualization, and prototyping. This human–AI co-creation model expands creative possibilities, accelerates production cycles, and lowers barriers to experimentation. However, it also raises questions about originality, authorship, and the cultural value of media products. The article further explores the complex implications of Generative AI for intellectual property. AI-generated or AI-assisted content challenges existing copyright frameworks, particularly regarding ownership, authorship, and liability. Media organizations must navigate risks related to training data transparency, potential infringement, and the protection of proprietary content. The study argues that effective governance strategies—combining legal compliance, ethical guidelines, and organizational policies—are essential for sustainable adoption. Overall, Generative AI represents both an opportunity and a strategic challenge for media organizations, requiring a balanced approach that integrates technological innovation with creative integrity and robust IP management.