Advanced Journal of Management, Humanity and Social Science

Abstract Background: Generative artificial intelligence (GenAI) can support explanation and feedback, but unrestricted answers may encourage cognitive offloading, weak source evaluation, and diminished learner authorship. This study developed and content-validated NEXUS, which restricts GenAI to Socratic coaching, and examined preliminary effectiveness for learning agency, intrinsic motivation, and critical-thinking disposition among male upper-secondary students in Qazvin, Iran. Methods: Phase I combined needs mapping in 463 students, co-design, cognitive interviews, and two-round Delphi validation by 15 experts. Phase II randomized 12 classes (106 students; six clusters per arm) to ten weekly NEXUS sessions or a dose- and technology-matched active control. Three-month follow-up was primary. Mixed-model estimates were paired with CR2 inference, CR3 sensitivity analysis, restricted wild-cluster bootstrap-t intervals, and exact within-pair randomization tests. Model-based Hedges g used total class-plus-student variance. Results: The 55-element manual achieved S-CVI/Ave = 0.91; universal agreement was 0.31, and Fleiss kappa was 0.66 for relevance and 0.61 for necessity. Follow-up completion was 89.6%. Adjusted follow-up differences were 0.44 for agentic engagement (95% CI 0.10 to 0.78; Holm-adjusted p = 0.042; g = 0.43), 0.31 for intrinsic motivation (95% CI -0.04 to 0.66; adjusted p = 0.086; g = 0.30), and 6.1 for critical-thinking disposition (95% CI 0.6 to 11.6; adjusted p = 0.078; g = 0.38). The exact global multivariate randomization test was p = 0.031. Fidelity averaged 86.8%; minor AI-output and participant-burden events occurred, but no serious related event was observed. Conclusion: NEXUS showed credible content validity and a promising joint signal, strongest for agentic engagement. With only 12 clusters, incomplete blinding, and no prospective registration, independent replication is required.

Abstract Background: Artificial intelligence (AI) is increasingly integrated into sports science, offering novel tools for injury surveillance, performance optimization, and psychological well-being monitoring in athletes. Despite growing adoption of machine learning (ML), computer vision, and wearable-sensor technologies, evidence regarding their clinical effectiveness and translational value remains fragmented across disciplines.

Objective: This article reviews and synthesizes current scientific evidence on the application of AI-based systems for reducing musculoskeletal injury risk and enhancing physical and psychological performance in athletic populations, with emphasis on methodological rigor and practical implementation.

Methods: A narrative synthesis was conducted drawing on systematic reviews, scoping reviews, and primary studies indexed in PubMed, Scopus, Web of Science, IEEE Xplore, and SPORTDiscus, focusing on peer-reviewed literature published between 2015 and 2026. Studies employing machine learning, deep learning, computer vision, wearable sensors, and AI-driven psychological monitoring in sport contexts were prioritized.

Results: Evidence indicates that AI-integrated wearable technologies and predictive analytics (e.g., random forests, convolutional neural networks, force-plate–derived biomechanical models) can identify injury risk factors, particularly in lower-limb musculoskeletal injuries, and support individualized rehabilitation protocols. Concurrently, AI-based facial expression analysis, physiological signal processing, and machine learning classifiers show emerging capacity to detect early markers of psychological stress and mental fatigue in elite athletes, complementing established sports-psychiatry frameworks. However, heterogeneity in study design, limited external validation, and "black-box" model interpretability remain significant barriers to clinical translation.

Conclusion: AI holds substantial promise for a proactive, individualized approach to athlete health management spanning biomechanical injury prevention and mental health surveillance. Future research should prioritize prospective validation, algorithmic transparency, and ethical frameworks for real-world deployment in athletic populations.

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: The tourism industry is undergoing a rapid transformation due to the integration of smart technologies. Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR) are increasingly used to enhance destination marketing and improve tourist experiences. These technologies offer opportunities for personalized marketing, immersive destination previews, real-time engagement, and sustainable branding, reshaping how tourists perceive and interact with destinations.

Objective: This study aims to analyze the efficiency and impact of AI, VR, and AR in destination marketing, evaluating their contribution to enhancing tourist satisfaction, engagement, and intention to visit. The study also investigates challenges and limitations associated with adopting smart technologies in tourism contexts.

Methodology: A systematic literature review was conducted, synthesizing findings from 50 recent empirical and theoretical studies (2018–2025) across tourism, marketing, and technology journals. The review focused on AI applications in personalized marketing, VR/AR tools for pre-visit and on-site experiences, and the integration of these technologies into smart destination ecosystems. Key metrics included tourist engagement, satisfaction, travel intention, and destination image.

Results: Findings indicate that AI significantly improves marketing efficiency by enabling data-driven personalization and predictive analytics. VR provides immersive pre-visit experiences that increase destination familiarity and travel intention, while AR enhances on-site engagement, learning, and satisfaction. Integrating these technologies within a smart destination ecosystem further strengthens operational efficiency and sustainable branding. Challenges include high implementation costs, accessibility barriers, user experience design, and privacy concerns.

Conclusion: Smart technologies (AI, VR, AR) demonstrate substantial potential in transforming destination marketing and tourist experiences. Destinations adopting these tools can enhance visitor engagement, satisfaction, and loyalty while promoting sustainable and authentic branding. Careful planning, user-centric design, and ethical data management are essential to maximize benefits and overcome limitations. Future research should examine longitudinal effects, cross-cultural differences, and measurable impacts on destination competitiveness.

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 The rapid evolution of artificial intelligence (AI) has transformed strategic decision-making processes in organizations, enabling faster insights, predictive analytics, and automation. However, the integration of AI systems also raises critical challenges concerning data governance, ethics, and regulatory compliance. Effective data governance ensures that data used in AI-driven decisions is accurate, secure, transparent, and aligned with corporate objectives. Ethical frameworks are essential to mitigate biases, prevent discrimination, and maintain trust among stakeholders. Moreover, the establishment of AI guidelines, including accountability, explainability, and fairness, plays a pivotal role in guiding organizations toward responsible innovation. This paper explores how robust data governance structures and ethical AI guidelines can enhance strategic decision-making and corporate integrity. It analyzes current models of governance frameworks, including ISO/IEC 38505 and OECD AI Principles, and their implications for business strategy. The discussion emphasizes that organizations adopting transparent and ethical data practices not only comply with regulations but also gain a competitive advantage through enhanced reputation and stakeholder confidence. Ultimately, the study concludes that integrating governance and ethics into AI decision systems is no longer optional—it is a strategic necessity for sustainable, fair, and accountable business practices in the era of intelligent automation.

Abstract Artificial Intelligence (AI), as one of the most advanced technologies of the 21st century, has increasingly entered the world's judicial systems and has transformed judicial decision-making processes. With its ability to analyze large volumes of data, identify complex patterns, and predict outcomes, this technology enables the acceleration and increase of accuracy in the process of handling cases. In this regard, AI can help judges and lawyers make more accurate and objective decisions based on evidence and information, thereby promoting judicial justice. In addition, the use of intelligent systems in predicting the risk of committing a crime or returning to the criminal justice system greatly contributes to decisions related to conditional release or setting bail. Also, text analysis and behavioral pattern recognition tools in criminal and legal cases can reduce the possibility of human errors and unintentional biases. However, the use of AI in decriminalization also comes with significant challenges and concerns. The most important of these challenges is the issue of transparency and understandability of algorithms; because AI decisions may be ambiguous and uninterpretable for human users and even judges due to technical complexities. Also, the risk of algorithmic discrimination resulting from inappropriate training data or biases in the data can jeopardize judicial justice. Concerns related to privacy, data security, and liability in the event of errors are also important concerns. Ultimately, the impact of AI on judicial decision-making processes depends largely on how it is designed, monitored, and the legal and ethical frameworks governing its use.

Abstract In the contemporary digital landscape, libraries stand at a critical crossroads between tradition and innovation. Once perceived as physical repositories of books and manuscripts, libraries today are undergoing a profound transformation in their structure, services, and purpose. In the digital transformation era, libraries are increasingly adopting emerging technologies to enhance their information management processes. This paper explores the integration of Artificial Intelligence (AI), cloud computing, blockchain, the Internet of Things (IoT), and mobile technologies in modern library environments. These technologies have significantly improved the efficiency of cataloging, information retrieval, resource sharing, and user interaction. By implementing AI-driven search tools, cloud-based platforms for digital preservation, blockchain for secure transactions, and IoT for inventory management, libraries have evolved into intelligent information centers. The study is based on a review of recent literature and practical applications from global library systems. It also highlights key challenges such as implementation costs, data privacy concerns, and the digital divide among users. Overall, the paper emphasizes that strategic and ethical adoption of emerging technologies can transform libraries into inclusive and efficient hubs for knowledge dissemination and community engagement in the 21st century.

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.