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Journal articles on the topic 'AI-driven personalization'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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Singh, Navdeep. "AI-Driven Personalization in eCommerce Advertising." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1692–98. http://dx.doi.org/10.22214/ijraset.2023.57695.

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Abstract: In the dynamic realm of eCommerce, the integration of Artificial Intelligence (AI) has revolutionized advertising strategies, forging a path towards highly personalized consumer experiences. This exploration delves into the multifaceted role of AI in eCommerce advertising, highlighting the efficacy of technologies such as machine learning, natural language processing, and predictive analytics. A thorough analysis of consumer behavior, underpinned by AI, reveals advancements in data collection, privacy concerns, and innovative data analysis techniques. Ethical considerations, including data privacy and bias in AI algorithms, emerge as pivotal in maintaining consumer trust. The paper presents an array of case studies, illustrating the successful application of AI across diverse industries.

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Enoch, Oluwademilade Sodiya, Oladipupo Amoo Olukunle, Joseph Umoga Uchenna, and Atadoga Akoh. "AI-driven personalization in web content delivery: A comparative study of user engagement in the USA and the UK." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 887–902. https://doi.org/10.5281/zenodo.14008497.

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In the ever-evolving landscape of digital experiences, AI-driven personalization has emerged as a pivotal force shaping how users interact with web content. This study conducts a comparative analysis of AI-driven personalization strategies in web content delivery, focusing on user engagement in the United States (USA) and the United Kingdom (UK). The research delves into the nuanced ways in which AI algorithms tailor web content to individual user preferences, examining the impact on user engagement metrics such as time spent on site, click-through rates, and conversion rates. Through a meticulous examination of AI-driven personalization practices employed by web platforms in both regions, this study seeks to identify common trends, regional differentiators, and their implications on user engagement. Key factors considered include the ethical dimensions of personalization, the adaptability of AI algorithms to diverse user behaviors, and the fine balance between customization and privacy concerns. The findings aim to contribute valuable insights to the fields of AI-driven web content delivery, user experience design, and the global discourse on the intersection of technology, personalization, and user engagement.

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Mahaboobsubani, Shaik. "AI-Driven Personalization in Hospitality Booking Platforms." Journal of Scientific and Engineering Research 8, no. 10 (2021): 223–30. https://doi.org/10.5281/zenodo.14356522.

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The use of AI in the hospitality sector has moved booking platforms into a direction of offering extremely personalized experiences. The article discusses how AI-driven recommendation systems produce booking options tailored to user preferences as a way to further increase engagement and user satisfaction. Different machine learning models are investigated to assess the effectiveness in predicting user behavior and preference, including collaborative filtering, content-based filtering, and hybrid approaches. Comparative studies indicate that the level of engagement and booking conversion is significantly higher on AI-powered sites compared to non-AI systems. This article also addresses several issues, such as data privacy, algorithmic bias, and personalization accuracy, in relation to user trust. Informed by insights on user-centric AI solutions, this study has attempted to point to the transformative impact of AI on hospitality booking platforms and will open vistas for intelligent, adaptive systems.

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Enoch Oluwademilade Sodiya, Olukunle Oladipupo Amoo, Uchenna Joseph Umoga, and Akoh Atadoga. "AI-driven personalization in web content delivery: A comparative study of user engagement in the USA and the UK." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 887–902. http://dx.doi.org/10.30574/wjarr.2024.21.2.0502.

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Verma, Shruti, and Dr Sabeeha Fatma. "How Personalization and AI Are Transforming Digital Marketing Campaigns." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42751.

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In the rapidly evolving digital marketing landscape, personalization and artificial intelligence (AI) have emerged as transformative forces, revolutionizing how brands engage with their audiences. This paper explores the integration of AI-driven personalization in digital marketing campaigns, highlighting its impact on consumer experience, brand loyalty, and overall campaign effectiveness. AI-powered algorithms leverage vast amounts of data to analyze consumer behavior, preferences, and purchasing patterns, enabling brands to deliver highly relevant content, product recommendations, and targeted advertisements in real time. Machine learning models, predictive analytics, and natural language processing (NLP) enhance personalization by automating customer interactions through chatbots, virtual assistants, and AI-driven email marketing campaigns. Additionally, AI facilitates dynamic content optimization, ensuring that marketing messages resonate with individual users based on their browsing history, location, demographics, and psychographics. The benefits of AI-driven personalization extend beyond enhanced user experience; businesses experience increased conversion rates, improved customer retention, and higher return on investment (ROI). However, challenges such as data privacy concerns, ethical considerations, and the need for continuous algorithm refinement must be addressed to ensure responsible AI implementation. This study underscores the necessity for brands to adopt AI-driven personalization strategies to maintain a competitive edge in the digital marketing sphere. As AI technology continues to evolve, its role in shaping customer-centric marketing campaigns will become even more significant, paving the way for hyper-personalized, automated, and data-driven marketing strategies that foster meaningful customer relationships.

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Nnenna Ijeoma Okeke, Olufunke Anne Alabi, Abbey Ngochindo Igwe, Onyeka Chrisanctus Ofodile, and Chikezie Paul-Mikki Ewim. "AI-driven personalization framework for SMES: Revolutionizing customer engagement and retention." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 2019–35. http://dx.doi.org/10.30574/wjarr.2024.24.1.3208.

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In today's competitive business landscape, Small and Medium Enterprises (SMEs) face unique challenges in building and maintaining strong customer relationships. An AI-driven personalization framework offers a transformative solution by enabling SMEs to deliver highly targeted and individualized customer experiences, improving both engagement and retention rates. This review outlines how artificial intelligence (AI) can empower SMEs by integrating data-driven insights with customer interaction processes to revolutionize business practices. AI-driven personalization leverages machine learning algorithms, natural language processing (NLP), and predictive analytics to analyze customer behaviors, preferences, and feedback. By aggregating data from various sources—such as online interactions, purchase histories, and social media activity—AI can generate personalized recommendations, offers, and communication strategies that resonate with individual customers. The framework facilitates dynamic customer segmentation, allowing SMEs to tailor marketing efforts and enhance service delivery. The personalization process also extends beyond marketing by optimizing customer support through AI-powered chatbots and recommendation systems, which provide real-time solutions and advice. This level of personalization fosters stronger emotional connections, increasing customer satisfaction and brand loyalty. Additionally, AI-driven insights enable SMEs to anticipate customer needs, predict churn rates, and proactively address potential issues, thereby boosting retention rates. For SMEs, the implementation of an AI-driven personalization framework is not only cost-effective but scalable, making it accessible even to businesses with limited resources. As SMEs increasingly adopt digital tools, the integration of AI-based personalization becomes essential for staying competitive in a rapidly evolving market. This review highlights the potential of AI in transforming customer engagement strategies for SMEs by offering a tailored, efficient, and sustainable approach to enhancing customer experiences.

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Nnenna, Ijeoma Okeke, Anne Alabi Olufunke, Ngochindo Igwe Abbey, Chrisanctus Ofodile Onyeka, and Paul-Mikki Ewim Chikezie. "AI-driven personalization framework for SMES: Revolutionizing customer engagement and retention." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 2019–35. https://doi.org/10.5281/zenodo.15051414.

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Thajchayapong, Ploy, and Ashok K. Goel. "Personalized Learning through AI-Driven Data Pipeline." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 111–14. https://doi.org/10.1609/aaaiss.v5i1.35572.

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The integration of artificial intelligence (AI) in education holds significant promise for transforming personalized learning. By analyzing student learning data, AI systems can adapt instruction to meet individual needs through tailored content, adaptive learning paths, real-time feedback, and continuous improvement loops. However, effective personalization at scale demands not only access to large volumes of learner data but also robust data architectures to collect, organize, standardize, and analyze that data in a secure and meaningful way. However, note that the ability of AI to personalize learning requires data about the learner and prior learning. Personalization at scale requires data at scale. The Architecture for AI-Augmented Learning (A4L) frame-work addresses these needs by establishing a comprehensive data pipeline that supports AI-driven personalization. This pipeline introduced capabilities for direct data ingestion, anonymization, and standardization, as well as integrated analytics and visualization pipelines to deliver actionable insights to educators and learners alike.

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Mandagie, Wenny Candra, and Robert Kristaung. "The power of AI personalization: Mediated moderation in social & e-commerce." Jurnal Manajemen dan Pemasaran Jasa 18, no. 1 (2025): 35–58. https://doi.org/10.25105/v18i1.21587.

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This study investigates the impact of AI-driven personalization on key customer outcomes within Indonesia's e-commerce landscape. It examines how personalization influences customer engagement, trust, perceived relevance, customer experience, and purchase frequency. While prior studies have extensively explored AI personalization, limited research has examined its mediated moderation effects on customer behavior in emerging e-commerce markets, particularly in Indonesia. This study addresses the gap by analyzing the moderating effect of education level and the mediating role of perceived relevance in shaping purchase frequency. Using a purposive sampling, data were collected from 251 e-commerce users through structured surveys. The data were analyzed using PLS-SEM. The findings reveal that AI-driven personalization significantly enhances customer engagement, trust, perceived relevance, and customer experience. Additionally, perceived relevance mediates the relationship between personalization and purchase frequency, emphasizing its role in driving consumer behavior. The education level moderates the relationship between customer experience and purchase frequency, suggesting that personalization strategies should be tailored to different educational backgrounds. It contributes to the AI personalization literature by uncovering its moderated mediation effects in social and e-commerce contexts, particularly in emerging markets. Practically, these insights can guide e-commerce practitioners in leveraging AI-driven personalization to enhance customer engagement, trust, and satisfaction, leading to increased purchase frequency. This study has limitations, including potential measurement biases in customer trust and experience, and excluding other influencing factors such as price sensitivity or brand loyalty. Future research should explore additional moderators and mediators, investigate AI personalization across various industries, and examine longitudinal effects to strengthen the findings.

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Suresh Kumar Maddala. "AI-driven personalization in consumer goods and retail: A technical analysis." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 458. https://doi.org/10.30574/wjarr.2025.26.2.1639.

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AI-driven personalization has become a critical competitive advantage in modern retail environments, enabling tailored customer experiences across digital and physical touchpoints. This article explores the transformative role of artificial intelligence technologies in reshaping consumer goods and retail personalization strategies. Beginning with an overview of fundamental AI personalization technologies, the discussion progresses through advanced recommendation engine architectures, dynamic pricing implementations, conversational AI systems, and in-store personalization solutions. The article examines how these technologies create cohesive personalized experiences that increase engagement, drive sales, and foster customer loyalty while addressing technical challenges and implementation considerations for retailers navigating the evolving digital commerce landscape.

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Shingh, Priyanshu. "The Relationship Between AI-Driven Personalization and Consumer Behaviour in Digital Marketing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50421.

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Abstract This comprehensive research examines the transformative impact of artificial intelligence-driven personalization on consumer behavior within the digital marketing landscape. The study reveals that AI-driven personalization has fundamentally altered how consumers interact with brands, creating sophisticated expectations for tailored experiences while driving significant improvements in business outcomes. Through analysis of current market trends, consumer preferences, and technological capabilities, this research finds that 92% of organizations worldwide are implementing AI-driven personalization strategies, with leading companies achieving 10-20% improvements in sales ROI.

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KHAN, PAREESHA. "Role of Ai in Personalizing Customer Experience." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50817.

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1. Introduction In today’s technology-driven marketplace, customer experience (CX) is a primary differentiator for businesses. Artificial Intelligence (AI) has emerged as a transformative force in enhancing how companies understand, serve, and engage with customers. This project aims to analyse the role of AI in delivering personalized experiences across sectors like e-commerce, banking, hospitality, retail, and telecommunications. 2. Objectives of the Study · To examine how AI tools enhance customer experience through personalization. · To explore various AI technologies used by businesses for tailoring customer interactions. · To assess the impact of AI-enabled personalization on customer satisfaction and brand loyalty. · To investigate the challenges businesses, face while implementing AI in CX. · To collect primary data via surveys to evaluate consumer experiences and concerns regarding AI-driven personalization.

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Lessa, Guilherme de Abreu. "THE IMPACT OF AI-DRIVEN PERSONALIZATION ON UX/UI DESIGN: NAVIGATING ETHICAL CONSIDERATIONS AND DATA-DRIVEN PRACTICES." Revista ft 29, no. 141 (2024): 58–59. https://doi.org/10.69849/revistaft/ra10202412200958.

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The integration of artificial intelligence (AI) into UX/UI design has revolutionized how digital interfaces interact with users, enabling personalized, adaptive, and user-centered experiences. This paper explores the transformative impact of AI-driven personalization on UX/UI design, emphasizing its role in enhancing user engagement, inclusivity, and interactivity. Key areas of focus include the intersection of AI technologies and information architecture, ethical considerations surrounding data privacy and algorithmic transparency, and the challenges of implementing AI in diverse design environments. Through detailed analysis and real-world case studies, this work highlights both the opportunities and potential pitfalls of leveraging AI for personalization. Future trends, such as conversational interfaces and augmented reality, are discussed, providing insights into the evolving landscape of AI-enhanced design. This study aims to equip designers, developers, and stakeholders with a comprehensive understanding of the implications and applications of AI-driven personalization in UX/UI design, fostering a balance between innovation and ethical responsibility.

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Amit Ojha. "Operationalizing LLMs in Retail: A framework for scalable AI-driven personalization." World Journal of Advanced Engineering Technology and Sciences 16, no. 1 (2025): 171–79. https://doi.org/10.30574/wjaets.2025.16.1.1201.

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The retail industry is undergoing a profound transformation driven by the convergence of artificial intelligence (AI) and massive-scale language models. This review examines the operationalization of large language models (LLMs), such as GPT-4 and LLAMA, in the context of scalable AI-driven personalization for retail environments. We present a comprehensive analysis of current architectures, methodologies, and use cases, while introducing the R2P-LLM (Real-time Responsive Personalization using Large Language Models) framework—a five-layer system designed to ensure modular, adaptive, and context-rich personalization. Drawing from experimental results and recent literature, we demonstrate that LLMs significantly outperform traditional and transformer-based systems in key performance areas, including click-through rate, conversion rate, and customer satisfaction. Additionally, the review addresses ethical, infrastructural, and deployment challenges, offering insights into future directions such as on-device inference, explainable AI, and multimodal personalization. The paper concludes that LLMs are not merely enhancements to personalization systems, but foundational technologies for next-generation, experience-driven commerce.

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Ankur Aggarwal. "Navigating the Complex Landscape of AI-Driven Personalization: Challenges and Considerations in the Generative AI Era." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2284–95. https://doi.org/10.32628/cseit2410612424.

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The emergence of generative AI has fundamentally transformed personalization systems, creating both unprecedented opportunities and significant challenges for organizations. This article examines the complex landscape of AI-driven personalization, focusing on four critical areas: privacy preservation, algorithmic bias mitigation, contextual dynamics, and user autonomy. Through analysis of industry practices, we explore how organizations are navigating these challenges while implementing effective personalization solutions. The article presents findings on privacy-first architectures, bias mitigation frameworks, adaptive system designs, and user empowerment tools, highlighting both technical and ethical considerations. The comprehensive review demonstrates that successful implementation of AI personalization systems requires a balanced approach that addresses privacy concerns while maintaining system effectiveness, mitigates algorithmic bias while preserving performance, adapts to evolving user contexts, and preserves user autonomy while delivering personalized experiences

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Anthonette Adanyin. "Rethinking black Friday: How Ai can drive 'small batch' personalized deals." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 2913–24. http://dx.doi.org/10.30574/wjarr.2024.21.1.2611.

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This paper discusses how Artificial Intelligence can revolutionize the traditional Black Friday retail event by making personalized deals in 'small batches'. In the ever-changing retail landscape due to changed consumer behavior and the technological advancement of emerging technologies, this study probes how AI-driven personalization can further drive effective and customer-centric promotional strategies. The paper begins by putting into perspective the evolution of Black Friday from a one-day event into an extended shopping period, noting the shortcomings that retailers find themselves facing regarding inventory management, demand forecasting, and profitability of large-scale promotions. These challenges require innovative solutions; AI-powered personalization has become a solution. A critical review of AI applications in retail and personalization with the 'small batch' deal capability, facilitated by AI algorithms, can provide personalized offers to individual customers or small micro-segments in correspondence with their preferences and behaviors. Some salient features of this approach include hyper-personalization, dynamic pricing, and contextual relevance ingredients for precision targeting and engagement. The paper details the technical implementation involved in AI-powered deals to help draw insights into data collection, management, and the algorithms at play in driving personalization. It also categorically helps in considering several critical ethical and consumer privacy concerns that businesses must pursue in integrating AI in retail. Case studies of Nike, Nordstrom, and Best Buy illustrate how AI-driven personalization enhances customer engagement, increases conversion rates and boosts sales performance. It takes into consideration some of the challenges considered, from technical limitations to data quality, and balancing personalization with inclusivity after potential solutions and best practices are reviewed. The future of retail personalization is considered, anticipating that continuous, year-round personalized shopping experiences will shift the need away from traditional events like Black Friday. In all, AI-driven 'small batch' deals promise a glittering future of Black Friday and retail promotions with the capability of offering more relevant and profitable experiences to shoppers. This paper adds to the emergent literature on AI in retail and gives practical insights into advanced personalization that retailers might want to implement. Future research areas are also identified, such as the long-term effects of hyper-personalization and the ethical implications of AI in retail.

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Sai Kumar Bitra. "Ethical AI and privacy in digital personalization: balancing personalization and user trust." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 774–79. https://doi.org/10.30574/wjaets.2025.15.2.0596.

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This article explores the complex intersection of artificial intelligence, personalization, and privacy in digital environments, exploring how organizations can effectively balance personalized user experiences with ethical considerations and regulatory compliance. The article shows key challenges in this domain, including regulatory frameworks like GDPR and CCPA, ethical concerns such as algorithmic bias and discrimination, and the growing importance of zero-party data as a user-centric approach to data collection. The article further analyzes how explainable AI (XAI) frameworks can address the "black box" nature of AI systems while building user trust. Through article analysis of current literature and industry practices, this article provides strategic recommendations for implementing responsible AI personalization that respects user privacy, maintains transparency, and establishes trust-based relationships between organizations and their users in an increasingly AI-driven digital ecosystem.

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Hayrapetyan, David R., and Syuzi H. Darbinyan. "The role of personalization in influencing digital trust and impulse buying in e-commerce." Theoretical and Experimental Psychology 18, no. 2 (2025): 120–37. https://doi.org/10.11621/tep-25-15.

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Background. In the context of the rapid growth of digital commerce and active implementation of artificial intelligence (AI) in marketing practices, personalization turns to be one of the key strategies for interacting with consumers. In the context of e-commerce personalization is considered as individualized product recommendations, price offers and advertising messages based on the user data and AI algorithms. This transforms consumers behavior, increasing their engagement, but at the same time boosting the risks of manipulative influence. In this regard, it seems relevant to analyze how personalization affects the formation of digital trust and consumer behavior. Objective. The study had its purpose to assess how personalization affects consumer trust and behavior. Study participants. The study involved 140 digital consumers (including 100 women) aged 20 to 40 years (mean age 28 years). Methods. The study implemented a survey method using a structured online questionnaire distributed using a simple random sampling method. Statistical data processing included cluster analysis, Pearson’s Chi-Square test, and graphical visualizations to identify trends. Results. The data analysis allowed to reveal three consumer segments in the sample under study which are characterized by a specific consumer behavior: 1) Security-Conscious Consumers value trusted brands and transparent personalization, 2) Price-Sensitive Consumers respond well to discount-driven personalization but exhibit lower impulse tendencies, and 3) Impulse Buyers are highly influenced by AI-driven recommendations and urgency-based marketing. Conclusion. Personalization can foster trust or drive impulsive buying, depending on consumer type. Business should implement ethical AI-driven personalization, enhance trust through transparency, and balance personalization with consumer autonomy.

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Pushkar, Mehendale. "Personalization in Education through AI." European Journal of Advances in Engineering and Technology 10, no. 3 (2023): 60–65. https://doi.org/10.5281/zenodo.12789606.

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Personalized learning, aided by artificial intelligence (AI), is a groundbreaking shift in education that abandons traditional one-size-fits-all approaches. This paper investigates the impact of AI on personalized education, particularly emphasizing how adaptive learning systems can enhance student engagement, motivation, and academic performance. A comprehensive review of current research and case studies highlights the technological, pedagogical, and ethical implications of implementing AI-driven personalized learning. The findings underscore the potential of AI to revolutionize education by creating more inclusive and effective learning environments, where each student's individual needs, learning styles, and aspirations are accommodated, thereby promoting equitable access to quality education and fostering the development of lifelong learners who are equipped to thrive in the rapidly evolving world.

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Gaikhe, Rushikesh. "Virtual Fashion with AI Personalization." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1170–79. https://doi.org/10.22214/ijraset.2025.70326.

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Abstract: The rise of e-commerce in the fashion industry hasledtoanincreaseddemandforvirtualtry-onsolutions that enhance user experience. This paper presents a Virtual Fashion with AI Personalization system leveraging Generative Adversarial Networks(GANs)and deep learning techniques to provide a realistic and interactive digital try-on experience. The system enables users to upload images, select garments, and visualize theminreal-timewithpersonalizedfitting.Thisapproach addresses challenges in online apparel shopping, including size misfit and lack of customization, through AI-driven garment segmentation, pose estimation, and recommendation algorithms. By integrating cutting-edge deep learning techniques, this research demonstrates a robustandscalablesolutionforrevolutionizingtheonline fashion industry

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Al-Dmour, Hani, Rand Al-Dmour, Yazeed Al-Dmour, and Ahmed Al-Dmour. "Transforming international student recruitment." Journal of International Students 15, no. 8 (2025): 25–52. https://doi.org/10.32674/m2fmc286.

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In this study, we examine the role of AI-driven marketing in international student recruitment, focusing on how perceived usefulness, trust, and personalization influence decision-making. Grounded in the Technology Acceptance Model (TAM), the Trust-Based Decision-Making Model, and the Personalization‒Privacy Paradox, we studied how AI-powered recruitment tools—such as chatbots, predictive analytics, and personalized content—impact student engagement and enrollment intentions. Based on the responses from 350 prospective international students, the findings indicate that AI-driven marketing enhances student recruitment by improving accessibility, engagement, and transparency. However, trust in AI remains critical, as concerns about data privacy and algorithmic bias significantly influence students’ willingness to apply it. Additionally, AI-powered personalization significantly affects decision-making by making recruitment more efficient and tailored to students' preferences; however, it also raises ethical concerns regarding privacy and data protection.

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Siddharth Gupta. "Ethical Considerations in AI-Driven Personalization for eCommerce Platforms." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 287–94. https://doi.org/10.32628/cseit251112371.

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This article explores the critical ethical considerations surrounding AI-driven personalization in eCommerce platforms. It delves into the complexities of privacy and data protection, examining the challenges of data collection practices and regulatory compliance in an increasingly data-driven marketplace. The paper addresses the pervasive issue of algorithmic bias, discussing its potential impacts on product recommendations and pricing, while proposing methods for detection and mitigation. Transparency and accountability in AI systems are thoroughly examined, emphasizing the importance of clear communication and explainable AI in building consumer trust. The article also navigates the delicate balance between personalization and consumer autonomy, considering the ethical implications of nudge techniques and strategies to preserve user agency. Finally, it outlines practical approaches for implementing ethical AI in eCommerce, including the development of ethical guidelines, regular system audits, and collaboration with ethicists and consumer advocates. This comprehensive analysis provides valuable insights for eCommerce platforms seeking to harness the power of AI while maintaining ethical standards and fostering consumer trust in the digital retail landscape.

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S.Tamilmani. "Brand Loyalty in the Digital Age: The Role of Personalization and AI in Consumer Engagement." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 716–21. https://doi.org/10.52783/jisem.v10i32s.5404.

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In the rapidly evolving digital landscape, brand loyalty has undergone a significant transformation, driven by technological advancements and changing consumer expectations. This study explores the role of personalization and artificial intelligence (AI) in fostering consumer engagement and enhancing brand loyalty. With the rise of e-commerce, social media, and data-driven marketing strategies, businesses are leveraging AI-powered recommendation systems, chatbots, and predictive analytics to deliver highly personalized experiences (Grewal et al., 2020). This paper examines how these technologies influence consumer trust, satisfaction, and long-term commitment to brands. Through a systematic review of existing literature and empirical analysis, the study identifies key factors that contribute to sustained brand loyalty in the digital age. Findings suggest that AI-driven personalization enhances customer satisfaction and emotional connection with brands, ultimately leading to higher retention rates (Lemon &Verhoef, 2016). However, challenges such as data privacy concerns and algorithmic biases pose potential risks to consumer trust (Wirtz et al., 2018). The study concludes by providing strategic insights for marketers to optimize AI-driven personalization while maintaining ethical standards in digital marketing practices.

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Sai Kumar Bitra. "The Power of AI-Driven Personalization: Technical Implementation and Impact." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 476–83. https://doi.org/10.32996/jcsts.2025.7.3.54.

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AI-driven personalization represents a transformative force in customer engagement, utilizing advanced algorithms to deliver tailored experiences at individual levels. This article explores the architectural foundations, core algorithms, implementation challenges, evaluation frameworks, and industry-specific applications that power modern personalization systems. From collaborative filtering and deep learning networks to real-time processing engines and privacy-preserving techniques, the technological ecosystem supporting personalization continues to evolve rapidly. The discussion addresses how organizations overcome critical challenges including cold-start problems, data sparsity, and filter bubbles while measuring success through both technical and business metrics. By examining applications across e-commerce, media, finance, healthcare, education, and retail sectors, the content illuminates how domain-specific adaptations create value through dynamic pricing, adaptive interfaces, customized recommendations, and seamless omnichannel experiences.

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Supolia, Mohit. "AI-Driven Personalization and Recommendations for Web Design Resources." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44812.

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The integration of Artificial Intelligence (AI) into creative workflows is transforming web design by enabling more personalized and efficient experiences. This study explores the development of AI-driven recommendation systems that suggest icons, fonts, and images based on designers' preferences, project needs, and current design trends. The proposed framework combines collaborative filtering, content-based analysis, and contextual modeling to adapt to changing user requirements while encouraging both familiarity and creative exploration. An experimental study with 127 professional web designers showed a 37% reduction in element selection time, a 28% improvement in design coherence, and 82% reported enhanced creativity. Evaluation metrics included adoption rates, workflow efficiency, and creative diversity. Results highlight how AI recommendation systems can act as creative collaborators—enhancing, not replacing, human input. This work advances the field of AI- assisted design by presenting a balanced, adaptive framework that supports creative autonomy in modern web design. Keywords: Artificial intelligence, recommendation systems, web design, personalization, creative tools, user experience, workflow optimization.

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Raiyan Haider, Md Farhan Abrar Ibne Bari, Md. Farhan Israk Shaif, and Mushfiqur Rahman. "Engineering hyper-personalization: Software challenges and brand performance in AI-driven digital marketing management: An empirical study." International Journal of Science and Research Archive 15, no. 2 (2025): 1122–41. https://doi.org/10.30574/ijsra.2025.15.2.1525.

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In this empirical study, we delve into engineering hyper-personalization within AI-driven digital marketing management. We focus specifically on the software challenges encountered and their impact on brand performance. AI technologies are truly transforming marketing, offering capabilities like precise customer segmentation, personalized content delivery, and real-time analytics – essential tools for achieving hyper-personalization. While AI holds significant promise for creating highly relevant and effective campaigns, implementing it for hyper-personalization brings distinct software-related challenges. These include navigating data privacy, ensuring algorithmic transparency, and addressing biases. Overcoming these engineering obstacles becomes essential for leveraging AI effectively to enhance customer experiences, optimize campaign results, and ultimately build stronger brand loyalty and visibility. Our study offers insights into these specific challenges and their implications for businesses aiming to maximize brand performance through advanced AI personalization.

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Ünlü, Sudenaz Ceren. "Enhancing User Experience through AI-Driven Personalization in User Interfaces." Human Computer Interaction 8, no. 1 (2024): 19. http://dx.doi.org/10.62802/m7mqmb52.

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Artificial intelligence (AI) has revolutionized user interface (UI) design by introducing personalization techniques that cater to individual user preferences, behaviors, and contexts. This research explores the integration of AI-driven personalization in user interfaces to enhance user experience (UX), focusing on adaptive design, predictive analytics, and real-time customization. By leveraging machine learning algorithms and behavioral data, AI enables interfaces to evolve dynamically, aligning with the unique needs of each user. This study investigates the role of personalization in improving engagement, satisfaction, and efficiency across various applications, such as e-commerce platforms, healthcare systems, and educational tools. Additionally, it examines the challenges of implementing personalized interfaces, including privacy concerns, data ethics, and algorithmic bias. By addressing these challenges, the research aims to develop best practices for ethical AI integration in user-centered design. The findings contribute to the growing body of knowledge on AI’s transformative potential in creating intuitive, efficient, and user-friendly interfaces, ultimately redefining the standards for digital interaction.

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Divya, Chockalingam. "Personalization in Online Car Shopping: A Data-Driven Approach." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 1 (2024): 1–3. https://doi.org/10.5281/zenodo.15087123.

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The online car shopping experience has evolved significantly with advancements in artificial intelligence (AI) and big data analytics. Personalization has become a crucial component in enhancing user experience, driving customer engagement, and improving conversion rates. This paper explores the role of personalization in online car shopping, the challenges faced, and the data-driven solutions that enable a tailored shopping experience. Various aspects such as machine learning algorithms, recommendation systems, and predictive analytics are discussed, along with their impact on the automotive retail industry. The paper also examines the scope of personalization in future advancements, highlighting emerging trends such as blockchain integration and AI-driven price negotiation.

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Beem, Varun Reddy. "AI-Driven Personalization in Retail: Transforming Customer Experience Through Intelligent Product Recommendations." European Journal of Computer Science and Information Technology 13, no. 38 (2025): 117–31. https://doi.org/10.37745/ejcsit.2013/vol13n38117131.

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This technical article explores the transformative impact of artificial intelligence on retail personalization, focusing on how advanced AI solutions like Amazon Personalize and fine-tuned language models are revolutionizing product recommendations and customer engagement. It examines a case study of an online fashion retailer that implemented a hybrid personalization system, combining recommendation algorithms with generative AI for dynamic content creation. The multi-layered architecture captures subtle behavioral signals, processes them through sophisticated recommendation engines, and delivers contextually relevant product suggestions with personalized descriptions. The article analyzes the significant business outcomes achieved through this implementation and details the technical considerations that organizations must address when building similar systems, including data pipeline architecture, model training strategies, privacy controls, and experimentation frameworks. The article concludes by exploring emerging frontiers in retail personalization technology, including multimodal recommendation systems that integrate visual and textual data, emotion-aware personalization that adapts to customer mood, and cross-channel personalization that creates consistent experiences across all touchpoints.

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Mhatre, Asst.Prof. Rajavi. "E-commerce and Consumer Behaviour: A review of AI-powered personalization and market trends." International Journal of Advance and Applied Research 6, no. 25(B) (2025): 156–59. https://doi.org/10.5281/zenodo.15315551.

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<strong>Abstract:</strong> In the ever-changing world of electronic commerce (e-commerce), it is essential for online businesses to comprehend and adapt to changing consumer behaviours to achieve lasting success. This review examines the intersection of e-commerce and consumer behaviour, emphasizing the significant impact of Artificial Intelligence (AI)-enhanced personalization on market trends. The emergence of AI has transformed how e-commerce platforms interact with and meet the distinct preferences of consumers. AI-driven personalization methods utilize complex algorithms to analyse extensive datasets, allowing for the provision of highly customized and relevant content, product suggestions, and user experiences. This review investigates the sophisticated processes behind AI-enabled personalization, highlighting how it improves customer engagement, satisfaction, and loyalty. Additionally, the study looks into the key market trends influenced by AI in e-commerce. From chatbots and virtual assistants that facilitate smooth customer interactions to predictive analytics that enhance inventory management, AI is fostering innovation across numerous aspects of the online retail sector. The analysis also addresses the application of machine learning algorithms in predicting consumer preferences, simplifying the buying process, and cultivating a more personalized shopping experience. As e-commerce progresses, this review further considers the challenges and ethical issues tied to AI-driven personalization. Topics such as data privacy, algorithmic bias, and the careful balance between customization and intrusiveness are discussed to provide a thorough understanding of AI's wider effects on consumer behaviour. Ultimately, this review presents valuable perspectives on the intertwined relationship between e-commerce and consumer behaviour, illuminating the transformative influence of AI-enabled personalization and its effects on emerging market trends. As companies traverse the digital realm, it is crucial to comprehend and leverage the potential of AI-driven approaches to remain competitive and satisfy the changing demands of tech-savvy consumers.  

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Ibrahim A., Yusuf, Nana Usman Bature, Abdulmajeed Tajudeen I., and Mohammad Isyaka. "AI-DRIVEN DYNAMIC CONTENT CREATION AND MICRO-SMALL AND MEDIUM ENTERPRISES MARKET PERFORMANCE IN THE FEDERAL CAPITAL TERRITORY, ABUJA." Abuja Journal of Business and Management 3, no. 2 (2025): 218–26. https://doi.org/10.70118/ajbam-02-2025-128.

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This study investigated the effect of AI-driven dynamic content creation on market conversion rate of Micro, Small, and Medium Enterprises (MSMEs) in Nigeria's Federal Capital Territory (FCT). The primary objectives are to investigate how content diversity, update frequency, and personalization influence customer engagement and lead-to-sales conversion. An exploratory research design was employed, utilizing a structured questionnaire administered to 384 MSME operators selected through stratified random sampling. Using SPSS, regression models tested the effects of selected AI-driven content strategies on the digital performance metrics of sampled MSMEs. Findings revealed significant positive relationships between AI-driven content creation strategies and improved market conversion rates. Specifically, content diversity increased average time spent on websites, frequent updates boosted customer engagement, and personalization significantly enhanced lead-to-sales conversion rates. The study concluded that dynamic content creation is crucial for MSMEs to enhance customer interactions and compete effectively in the emerging digital marketplace. It was thus recommended that MSMEs invest in AI tools, focus on personalization, maintain regular content updates, and diversify content formats to optimize engagement. For enterprise development agencies, the study advised providing training, facilitating access to AI solutions, promoting AI adoption, and supporting research and development.

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Celestin, Mbonigaba. "AI-Driven Personalization: Revolutionizing E-commerce with Blockchain and IoT Collaboration." Randwick International of Social Science Journal 6, no. 2 (2025): 104–15. https://doi.org/10.47175/rissj.v6i2.1147.

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This study investigates the transformative potential of Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) in revolutionizing e-commerce personalization. Utilizing a qualitative methodology, it incorporates systematic literature reviews, secondary data analysis, and expert interviews. Findings reveal an 87.5% increase in customer retention rates due to AI-driven personalization from 2020 to 2024, with blockchain adoption reducing fraud cases by 80% and boosting transaction volumes by 540% over the same period. IoT integration achieved a ninefold rise in device use, driving a 45% reduction in logistics costs. The synergy of these technologies has addressed personalization challenges while enhancing security and operational efficiency. The study concludes with a call for adopting integrated frameworks, prioritizing ethical AI models, and scaling IoT adoption to sustain innovation in e-commerce.

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Julker Nain. "Ai-driven CRM systems in insurance: Personalization at scale." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2850. https://doi.org/10.30574/wjarr.2024.23.2.2523.

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The purpose of this research paper investigates artificial intelligence and data analytics phenomena which impact the financial services industry specifically in Customer Relationship Management systems implementation. This document examines contemporary CRM system development together with artificial intelligences in customer analytics and their practical and complex implementation challenges. This research explores how artificial intelligence enhances both personalization operations and customer information and decision-making through natural language processing and machine learning and predictive analysis study. The proof of AI-CRM system performance needs additional clarification based on current evidence from retail banking, wealth management businesses and insurance industries showing positive results. Data privacy aspects along with ethical AI utilization in finance and compliance requirements form essential points studied in the research. The research provides financial service providers with both present-time trends analysis and literature-validated guidelines about implementing AI into the CRM system to generate possibilities for future applications.

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Julker, Nain. "Ai-driven CRM systems in insurance: Personalization at scale." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2850–65. https://doi.org/10.5281/zenodo.14908920.

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Tyagi, Mukund, Dr Nikhil Sirohi, Dr Pallavi Tyagi, and Kajal Yadav. "Future Trends of B2C Marketing with Artificial Intelligence." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51309.

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Artificial Intelligence (AI) is fundamentally transforming business-to-consumer (B2C) marketing by enhancing the ability of brands to understand, engage, and retain customers. This paper investigates the emerging trends shaping the future of B2C marketing through the integration of advanced AI technologies. Key developments such as generative AI, conversational interfaces, emotion AI, and ethical personalization are enabling hyper-personalized content, real-time customer interactions, and deeper emotional engagement. These innovations are not only optimizing marketing strategies but also reshaping consumer expectations for seamless, intelligent, and value-driven experiences. As AI systems become more embedded in marketing operations, businesses must navigate critical challenges, including data privacy concerns, algorithmic bias, and the evolving landscape of AI regulation. This paper explores how marketers can leverage AI to deliver ethically responsible and consumer-centric campaigns while maintaining transparency and trust. It also highlights the strategic importance of aligning AI-driven marketing initiatives with consumer values and societal norms. By examining current practices and projecting future developments, this study provides actionable insights and strategic directions for marketers aiming to remain competitive in an AI-driven marketplace. Ultimately, the paper emphasizes the need for continuous innovation, ethical consideration, and human-AI collaboration to shape the future of B2C marketing effectively. Keywords: Artificial Intelligence, B2C Marketing, Hyper-Personalization, Generative AI, Ethical Personalization, Consumer Behaviour

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Sipos, Dario. "The Effects of AI-Powered Personalization on Consumer Trust, Satisfaction, and Purchase Intent." European Journal of Applied Science, Engineering and Technology 3, no. 2 (2025): 14–24. https://doi.org/10.59324/ejaset.2025.3(2).02.

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The integration of Artificial Intelligence (AI) in e-commerce has dramatically reshaped how businesses interact with consumers. AI-driven personalization is considered among the most powerful strategies for increasing consumer engagement, loyalty, and overall profitability. Practitioners have questioned its impact on consumer trust, satisfaction, and purchase intent due to growing concerns over privacy. This study offers a quantitative analysis involving 473 respondents to explore how AI-powered personalization influences these key consumer outcomes. Employing structural equation modeling, the findings indicate that AI-based personalization significantly improves trust and satisfaction, with satisfaction acting as a significant mediator for purchase intent. Privacy concerns stand out as a critical moderating factor, potentially hindering the positive effects of personalization on trust and subsequent purchase behaviors. We conclude the study with recommendations on ethical data practices, transparent communication, and regulatory frameworks to build and maintain consumer trust in an era of AI personalization.

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Parhlad, Singh "Ahluwalia." "AI-Driven Personalization of Patient Hospitality Services: Enhancing Comfort and Care in Healthcare Facilities." Siddhanta's International Journal of Advanced Research in Arts & Humanities 2, no. 4 (2025): 60–71. https://doi.org/10.5281/zenodo.15234201.

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Artificial Intelligence (AI) has become a transformative force in healthcare, offering innovations not only in clinical treatments but also in patient hospitality services. The integration of AI into healthcare environments has the potential to personalize patient care, making the overall experience more comfortable, efficient, and responsive to individual needs. This paper explores the application of AI-driven personalization in patient hospitality services, highlighting its impact on patient comfort and satisfaction. By examining existing literature and case studies, the paper discusses how AI technologies can optimize room settings, tailor meal options, personalize communication, and enhance overall service delivery. The findings suggest that AI-driven personalization leads to improved patient satisfaction, better engagement, and ultimately, enhanced care outcomes.

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Kabashkin, Igor, Vladimir Perekrestov, and Maksim Pivovar. "AI-Driven Fault Detection and Maintenance Optimization for Aviation Technical Support Systems." Processes 13, no. 3 (2025): 666. https://doi.org/10.3390/pr13030666.

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This study investigates the integration of customization and personalization approaches in aviation maintenance through Aviation Technical Support as a Service (ATSaaS) platform. Through a comprehensive survey of 86 small and medium-sized airlines, combined with mathematical modeling of fault detection systems, the study develops and validates a hybrid framework that integrates traditional maintenance approaches with AI-driven solutions. The comparative analysis demonstrates that the hybrid model significantly outperforms both pure customization and pure personalization approaches, achieving a 95% fault detection rate compared to 75% for customization-only and 88% for personalization-only models. The hybrid approach also showed superior performance in predictive maintenance effectiveness (96%), operational downtime reduction (92%), and cost optimization (90%). The research presents three architectural frameworks for ATSaaS implementation—customization-based, personalization-based, and hybrid—providing a structured approach for different airline categories. Large airlines, with their extensive technical expertise and complex operational requirements, benefit from enhancing their customized maintenance programs with personalization tools, improving overall maintenance efficiency by 23%. Simultaneously, smaller operators, often constrained by limited resources, can use ATSaaS platforms to access sophisticated maintenance capabilities without extensive in-house expertise, reducing operational costs by 35% compared to traditional approaches. The study concludes that the successful integration of customization and personalization through ATSaaS platforms represents a promising direction for optimizing aviation maintenance operations, supporting the industry’s movement toward data-driven, adaptive maintenance solutions.

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Parab, Gautam Ulhas. "AI-DRIVEN PERSONALIZATION IN RETAIL ANALYTICS: TRANSFORMING CUSTOMER EXPERIENCES." INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY 7, no. 2 (2024): 2387–96. https://doi.org/10.34218/ijrcait_07_02_176.

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Earley, Seth. "The Problem of Personalization: AI-Driven Analytics at Scale." IT Professional 19, no. 6 (2017): 74–80. http://dx.doi.org/10.1109/mitp.2017.4241471.

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Das, Amit, Sanjeev Malaviya, and Manpreet Singh. "The Impact of AI-Driven Personalization on Learners Performance." International Journal of Computer Sciences and Engineering 11, no. 8 (2023): 15–22. https://doi.org/10.26438/ijcse/v11i8.1522.

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Venkateswaran Petchiappan. "AI-driven vehicle customization and personalization in automobile industry." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 157–68. https://doi.org/10.30574/wjaets.2025.15.3.0921.

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The automobile industry is experiencing a profound digital transformation with artificial intelligence emerging as a cornerstone technology reshaping customer experiences and operational paradigms. AI-powered vehicle selection and configuration systems represent transformative applications revolutionizing how consumers discover, personalize, and purchase vehicles. Modern automotive manufacturers leverage sophisticated data analytics platforms like SAP HANA, with in-memory computing capabilities processing configuration variables during real-time customer interactions. These systems analyze substantial volumes of customer data to deliver highly personalized vehicle configurations tailored to individual preferences, driving habits, and lifestyle requirements. The technological foundation incorporates machine learning, natural language processing, and big data analytics within unified customer data platforms, enabling remarkable improvements in customization accuracy and delivery timelines. The implementation methodologies span hyper-personalized vehicle configuration, dynamic pricing optimization, and fleet electrification strategies, resulting in significant operational efficiency improvements, customer experience enhancements, and sustainability impacts. Future directions include blockchain-verified vehicle customization, advanced AI methodologies, and integration of extended reality, promising to further revolutionize the automotive customization landscape through immutable configuration records, reinforcement learning models, and immersive configuration experiences.

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CHAUHAN, NEHA KUMARI. "The Impact of AI Driven Personalisation on Consumer Behaviour and Brand Loyalty." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50561.

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ABSTRACT The integration of artificial intelligence (AI) into modern business strategies is reshaping how brands interact with and understand their customers. This study explores the influence of AI-enabled specialization—where businesses utilize tools like machine learning, predictive analytics, and personalized algorithms—to customize offerings, services, and marketing approaches tailored to individual consumer profiles. As With the help of AI tools, companies can large-scale personalization, they are raising consumer expectations for seamless, context-aware, and intuitive experiences. While this shift opens up new avenues for enhancing customer satisfaction and fostering deeper brand relationships, it also introduces challenges. Increased personalization can lower switching costs and intensify competition, making it easier for consumers to shift brand preferences based on algorithmic comparisons. Artificial intelligence (AI) is transforming digital marketing by enabling firms to analyze vast amounts of consumer data and offer highly personalized experiences. As competition grows,companies increasingly adopt AI-driven strategies to boost engagement and foster brand loyalty. In this report there is a quantitative survey of 60 consumers and applied structural equation modeling (SEM) to examine how AI integration, personalization, consumer engagement, and brand loyalty are interrelated. The findings show that AI-drivenpersonalization significantly enhances customer experiences, intensifies consumer engagement, and encourages long-term brand loyalty. For example, personalized AI messages (H1, β=0.789) strongly increased perceived personalization, which in turn drove engagement (H2, β=0.777) and loyalty (H5, β=0.613), while engagement also boosted loyalty (H3, β=0.517). All hypothesized paths were supported. The study concludes that strategic use of ethical, transparent AI personalization in digital marketing can improve customer experience and competitive advantage. These insights have practical implications: marketers should invest in explainable AI and tailor marketing content to individual preferences to build stronger, trust-based relationships with consumers.

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S. Logalakshmi and S. Poornima. "AI-POWERED PERSONALIZATION ALGORITHMS FOR DIGITAL MARKETING." International Journal of Trendy Research in Engineering and Technology 09, no. 03 (2025): 45–49. https://doi.org/10.54473/ijtret.2025.9306.

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This research examines the impact of AI-powered personalization algorithms on digital marketing strategies. As customer engagement and content personalization become critical for business success, AI technologies like machine learning, data mining, and deep learning are employed to customize marketing tactics for individual consumers. The study investigates how these algorithms enhance the overall customer experience, increase marketing ROI, and improve conversion rates by offering personalized content in real-time. A survey of 81 participants from diverse industries assesses their views on personalized marketing and its efficiency. The analysis employs advanced statistical methods, such as correlation and regression analysis, to measure the relationship between AI-driven personalization and consumer engagement. The results indicate that AI personalization significantly enhances customer satisfaction and purchasing behavior.

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Snigdha, Esrat Zahan, MD Nadil khan, Kirtibhai Desai, Mohammad Majharul Islam, MD Mahbub Rabbani, and Saif Ahmad. "AI-Driven Customer Insights in IT Services: A Framework for Personalization and Scalable Solutions." American Journal of Engineering and Technology 07, no. 03 (2025): 35–39. https://doi.org/10.37547/tajet/volume07issue03-04.

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New developments in Artificial Intelligence (AI) in IT services have drastically altered how companies use customer insights to supply personalized and scalable responses to a wide variety of client necessities. The focus of this study consists in the use of AI tools and algorithms in customer data analysis, but also in the sense that they are useful for providing targeted and efficient IT service solutions. The findings are robust because a mixed-methods approach was employed, using qualitative analysis of case studies and quantitative evaluations of service outcomes. The results show that adding AI features into workflows of IT services can significantly improve satisfaction metrics for customer, operating efficiency, and the scalability of the service overall. Additionally, the paper organizes frameworks and different strategies for utilizing AI devices and investigating issues, for example, data secrecy, calculation predisposition, and extendibility. This research also helps bridge a few of the existing gaps in the existing body of knowledge about potential AI applications in customer–centric IT service and provides actionable insights for practitioners and policymakers. The main takeaways indicate how much organizations need to start seeing AI as a business growth strategy and not as a technological advancement. Related to this, future research needed to understand the ethical considerations of artificial intelligence in customer insights, and the overall implications of artificial intelligence, in the context of media distributors and different cultural and regulatory environments.

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Upasana, Das. "AI Powered Consumer Behavior in e-Commerce." International Journal of Contemporary Research in Multidisciplinary 3, no. 4 (2024): 01–13. https://doi.org/10.5281/zenodo.12679257.

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As the e-commerce industry continues to evolve, artificial intelligence (AI) has emerged as a powerful tool for understanding and influencing consumer behavior. This review explores the convergence of e-commerce and consumer behavior, emphasizing the significant influence of AI-driven personalization on market trends. AI-powered technologies have the ability to analyze vast amounts of data in real-time, providing valuable insights into consumer preferences, shopping patterns, and decision-making processes. By leveraging AI algorithms, e-commerce retailers can personalize the shopping experience, recommend products, and optimize pricing strategies to maximize sales and customer satisfaction. Additionally, AI can streamline the purchasing process by offering chatbots for customer support, virtual assistants for product recommendations, and predictive analytics for inventory management. The investigation explores how machine learning algorithms can be used to forecast customer preferences, expedite the purchase process, and create a more customized shopping experience. The review also examines the difficulties and moral dilemmas posed by AI-powered personalization as e-commerce develops. To give a thorough grasp of the wider ramifications of AI in influencing consumer behavior, issues like algorithmic bias, data privacy, and the careful balancing act between personalization and intrusiveness are studied. This analysis provides insightful information about the mutually beneficial interaction between e-commerce and consumer behavior, highlighting the revolutionary potential of AI-powered personalization and its impact on developing market trends. Businesses must comprehend and take advantage of AI-driven tactics in order to remain competitive as they navigate the digital landscape. This combination of data-driven insights and automated decision-making has the potential to revolutionize the way consumers interact with online retailers, ultimately shaping the future of e-commerce

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Jaiswal, Sanjana. "The Role of Artificial Intelligence in Personalizing Digital Marketing Strategies." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04415.

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Abstract: AI as machine learning, predictive analytics, and natural language processing to create highly personalized and targeted marketing campaigns. The study investigates how AI contributes to understanding consumer preferences, segmenting audiences, automating content delivery, and optimizing user experiences in real-time. Through a combination of literature review, case studies, and data analysis, the research highlights the effectiveness of AI-driven personalization in improving customer engagement, conversion rates, and brand loyalty. The findings demonstrate that AI not only enables marketers to anticipate consumer needs but also fosters deeper, data-driven relationships between brands and their customers. The study concludes with recommendations for marketers to strategically implement AI technologies while ensuring ethical use of consumer data. Keywords: Artificial Intelligence, Digital Marketing, Personalization, Predictive Analytics, Customer Engagement, Consumer Behavior

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Dalain, Ali, and Mohammad Yamin. "Examining the Influence of AI-Supporting HR Practices Towards Recruitment Efficiency with the Moderating Effect of Anthropomorphism." Sustainability 17, no. 6 (2025): 2658. https://doi.org/10.3390/su17062658.

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Technological developments are compelling organizations to upgrade their HR practices by adopting AI-driven applications. Yet, HR professionals are hesitant to adopt AI-driven technology in the recruitment process. Addressing this topic, the current study developed an amalgamated research framework for investigating factors relevant to AI, such as perceived interactivity, perceived intelligence, personalization, accuracy, automation, and real-time experience, which was applied to investigate employees’ intention to adopt AI-driven recruitment. For our data collection, survey questionnaires were distributed among HR professionals, which garnered 336 respondents. The empirical findings revealed that perceived interactivity, perceived intelligence, personalization, accuracy, automation, and real-time experience explained a large portion (89.7%) of the variance R2 in employees’ intention to adopt AI-driven recruitment practices. The effect size f2 analysis, then demonstrated that perceived interactivity was the most influential factor in employees’ intention to adopt AI-driven recruitment. Overall, this study indicates that perceived interactivity, perceived intelligence, personalization, accuracy, automation, and real-time experience are the core factors enhancing employees’ intention to adopt AI-enabled recruitment and should hence be the focuses of policymakers’ attention. Furthermore, this study uniquely unveils a new research framework that may be applied to improve the recruitment process in organizations by using artificial intelligence, which may empower HR professionals to hire the right staff efficiently and cost-effectively. Similarly, this study is in line with United Nations sustainable development goals and contributes to decent work, industry innovation, and sustainable economic growth by using artificial intelligence human resource practices.

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Prabin Adhikari, Prashamsa Hamal, and Francis Baidoo Jnr. "The role of big data analytics and management information systems in consumer personalization in U.S. Retail, banking and finance." International Journal of Science and Research Archive 14, no. 2 (2025): 1186–201. https://doi.org/10.30574/ijsra.2025.14.2.0388.

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The increasing reliance on Big Data Analytics (BDA) and Management Information Systems (MIS) has significantly transformed consumer personalization strategies across industries. Businesses are leveraging data-driven insights, artificial intelligence (AI), and predictive analytics to enhance customer experiences, optimize engagement strategies, and tailor services based on individual preferences. However, challenges such as data privacy, ethical concerns, and system integration issues remain critical considerations in the adoption of these technologies. This study aims to examine the role of BDA and MIS in consumer personalization, focusing on how businesses utilize these technologies to enhance customer engagement, predict behavior, and deliver personalized services in the retail, banking, and finance sectors. The study employs a systematic literature review to analyze existing research on BDA and MIS-driven personalization. It synthesizes findings from peer-reviewed journals, conference proceedings, and industry reports to provide a comprehensive understanding of technological advancements and their implications. The results indicate that BDA enhances real-time decision-making, predictive modeling, and hyper-personalization, while MIS enables seamless data integration and customer relationship management (CRM). However, concerns regarding data security, algorithmic bias, and compliance with privacy regulations remain significant challenges. BDA and MIS are critical enablers of consumer personalization, yet businesses must adopt ethical AI practices, strengthen cybersecurity measures, and ensure regulatory compliance to maximize their benefits. Organizations should invest in scalable AI-driven MIS platforms, enhance transparency in data usage, and leverage predictive analytics to create consumer-centric personalization strategies while prioritizing privacy and security.

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