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

Sindani Job Weindava
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Sindani Job Weindava: University of San Francisco

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 10, 2414-2426

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.

Date: 2025
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