Paper Title
AI-DRIVEN PERSONALISATION: TRANSFORMING USER EXPERIENCE ACROSS MOBILE APPLICATIONS

Abstract
AI-driven personalisation represents a transformative innovation in mobile application development, revolutionising user engagement across domains such as e-commerce, social media, education, and healthcare. By leveraging cutting-edge machine learning (ML) and deep learning frameworks, these systems deliver real-time, context-aware, and user-specific recommendations, significantly enhancing user interaction, retention, and satisfaction. This study provides a systematic review of foundational methodologies, including collaborative filtering, deep neural networks, and transformer-based architectures, examining their application across diverse industries. A particular focus is placed on multimodal and context-aware approaches that underpin adaptive, scalable, and privacy-conscious solutions. Using a comprehensive evaluation framework, this study quantifies the impact of personalisation systems on key performance indicators (KPIs) such as session duration, user retention rates, and conversion metrics. Critical ethical considerations, including data privacy, algorithmic fairness, and transparency, are rigorously analysed. To address these challenges, privacy-preserving strategies such as federated learning and differential privacy are advocated as essential tools for mitigating risks. Additionally, the pivotal role of Explainable AI (XAI) is explored, highlighting its potential to foster user trust and ensure compliance with regulatory standards such as GDPR and CCPA. Emerging advancements in computational efficiency, edge AI, and fairness-aware algorithms have been identified as essential enablers of next-generation personalised systems. By integrating these technological innovations with responsible AI practices, this paper envisions a future in which personalisation systems are aligned with human-centric values, fostering inclusivity, equity, and sustainable trust. The interplay between advanced neural architectures and ethical frameworks is critical to achieving these objectives. This study bridges technical innovation with ethical and practical considerations, and offers a comprehensive roadmap for researchers, industry practitioners, and policymakers. It emphasises the importance of transparency, fairness, and inclusivity in harnessing the transformative potential of AI-driven personalisation. Through the responsible development of these systems, key players can transform how users engage with mobile environments, thereby enhancing their experiences while maintaining integrity and fairness. Keywords - AI-driven personalisation, Machine Learning, Deep Learning, Ethical Frameworks, Explainable AI, Privacy-preserving strategies, Mobile ecosystems, User engagement, Algorithmic fairness, Transparency.