Engineering Serendipity: Reclaiming Joyful Discovery and Consumer Trust in Hyper-Personalized Ai Systems

Abstract

The widespread adoption of artificial intelligence (AI) in recommendation systems has revolutionized how users interact with content, commerce, and culture. However, the same hyper-personalization that enhances user relevance often suppresses discovery, autonomy, and delight—leading to consumer resistance and systemic homogenization. This study explores the phenomenon of engineered serendipity—the intentional design of systems that balance personalization with purposeful unpredictability. Drawing from cross-disciplinary literature in computer science, human–AI interaction, and behavioral engineering, we develop a conceptual framework and propose a roadmap for integrating serendipity as a measurable engineering objective. Our findings suggest that reintroducing controlled randomness, diversity-aware ranking, and transparent user controls can restore trust and joy in AI-mediated discovery. This work highlights the importance of aligning engineering design, consumer psychology, and ethical governance to reclaim human curiosity in an algorithmically filtered world.