Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
This article explores the transformative synergy between artificial intelligence and human creativity in revolutionizing e-commerce personalization. It examines how AI's computational power processes vast customer datasets to identify patterns invisible to human analysis alone, while human creativity contributes essential emotional intelligence, cultural understanding, and ethical oversight. The article analyzes the technical architecture enabling this collaboration through a layered approach comprising data collection, AI processing, human interface, and customer presentation components. Through evidence from multiple studies, the article demonstrates how this collaborative model delivers measurable business outcomes across conversion metrics, transaction values, customer retention, and marketing efficiency. It further investigates emerging technologies, including augmented reality, voice commerce, emotional AI, and blockchain-based personalization approaches that promise to further enhance the AI-human partnership. This study offers an integrated framework for AI-human collaboration in personalization, paving the way for future research on ethical AI implementations and cross-cultural applications in global e-commerce. By documenting both current implementations and future possibilities, this article provides a comprehensive examination of how the integration of technological capabilities with human ingenuity creates shopping experiences that are simultaneously data-driven and emotionally resonant, addressing both rational and emotional dimensions of consumer decision-making....
Occupational therapy (OT) is a client-centered health profession focused on enhancing individuals’ ability to perform meaningful activities and daily tasks, particularly for those recovering from injury, illness, or disability. As a core component of rehabilitation, it promotes independence, well-being, and quality of life through personalized, goal-oriented interventions. Identifying and measuring the role of artificial intelligence (AI) in the human–computer interaction (HCI) within OT is critical for improving therapeutic outcomes and patient engagement. Despite AI’s growing significance, the integration of AI-driven HCI in OT remains relatively underexplored in the existing literature. This scoping review identifies and maps current research on the topic, highlighting applications and proposing directions for future work. A structured literature search was conducted using the Scopus and PubMed databases. Articles were included if their primary focus was on the intersection of AI, HCI, and OT. Out of 55 retrieved articles, 26 met the inclusion criteria. This work highlights three key findings: (i) machine learning, robotics, and virtual reality are emerging as prominent AI-driven HCI techniques in OT; (ii) the integration of AI-enhanced HCI offers significant opportunities for developing personalized therapeutic interventions; (iii) further research is essential to evaluate the long-term efficacy, ethical implications, and patient outcomes associated with AI-driven HCI in OT. These insights aim to guide future research efforts and clinical applications within this evolving interdisciplinary field. In conclusion, AI-driven HCI holds considerable promise for advancing OT practice, yet further research is needed to fully realize its clinical potential....
This article examines the transformative role of cloud-native data engineering in facilitating human-AI collaboration within healthcare environments. It explores how modern data platforms from major cloud providers enable seamless integration of machine learning models into clinical workflows, supporting healthcare professionals in decision-making while maintaining human judgment as the cornerstone of patient care. The discussion encompasses the technical architecture of healthcare data pipelines, strategies for implementing real-time analytics, and approaches for integrating AI models that complement rather than replace clinical expertise. The article addresses critical challenges, including bias mitigation, data governance, and ethical considerations, advocating for responsible AI deployment that prioritizes patient outcomes. Through the examination of industry case studies and emerging trends, the article provides a comprehensive analysis of how cloud-native technologies are reshaping healthcare delivery while emphasizing the essential partnership between technology innovation and human expertise....
This article explores the symbiotic relationship between artificial intelligence systems and human researchers in revolutionizing customer behavior analysis within the financial services sector. By examining how AI's computational capabilities complement human contextual understanding, we demonstrate a framework where machine learning models process vast transactional datasets while human experts provide crucial interpretive insights regarding socioeconomic factors and cultural nuances. The resulting collaborative approach enables financial institutions to develop more sophisticated customer segmentation strategies, deliver precisely tailored product recommendations, and implement proactive retention measures through predictive churn analysis. This human-AI partnership represents a significant advancement over purely algorithmic or exclusively human-driven approaches, offering financial institutions a comprehensive methodology for enhancing customer engagement, improving service personalization, and ultimately driving business growth while addressing the complex needs of diverse customer bases....
People help those with a reputation for helping others; as a result, they are more likely to behave generously when reputational concerns are present. Because people are increasingly making helping decisions in the presence of both humans and AI in “hybrid systems,” here we ask whether and how reputation-based reciprocity (RBR) promotes generosity in human–bot networks, compared with human-only ones. In two experiments—one where interactants were embedded in a patterned indirect reciprocity network and either knew or did not know that bots were present and another entailing one-shot interactions between humans and bots—we demonstrate that RBR is significantly less effective at fostering generosity in hybrid systems. At the network level, people are less generous when they know bots are present. In line with prior work, our findings suggest that this is driven by altered norms about helping in (known) hybrid networks governed by RBR: people do not believe bots deserve help like humans do, reducing overall generosity. In one-shot dyadic interactions, we likewise demonstrate that people are less willing to help bots even when they can receive reputational rewards for helping and even toward bots that have reputations for helping humans (or bots). People are also less likely to help people who help bots (compared with people who help people) and punish people who fail to help bots (compared with people who fail to help people). Adding bots to RBR networks affects not only humans’ prosocial behavior, but also their evaluations of generosity toward human and bot alters....
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