Modern social technologies often paradoxically inhibit direct, meaningful human connection. We address this gap by proposing the Curated Interaction Model, a novel client-side framework designed to orchestrate engaging, face-to-face conversations among small groups of young adults (ages 21—29) in a controlled social setting. The model employs a lightweight, three-item psychometric classifier to assign participants to one of six empirically derived conversational profiles. Based on the specific compositional dynamics of a five- to six-person group, the frame-work’s orchestration algorithm selects and blends curated, versioned collections of discussion prompts ("decks"). This process adaptively guides the group toward balanced, high-quality dialogue. Critically, all orchestration logic executes on-device (edge-first), a design choice that minimizes cloud dependency, reduces interactional latency, and inherently preserves user privacy. The model’s architecture is grounded in established social-psychological principles, including optimal dissonance, conversational flow, and social identity theory, to foster emergent communion. In this paper, we formalize the orchestration algorithm, provide a rigorous termino-logical framework, and present extended system visualizations, including client server data flow and a session state transition diagram. We offer implementation sketches in Swift/Objective-C and delineate a comprehensive evaluation plan, complete with statistical power considerations. We conclude by proposing empirical validation pathways (simulation and pilot studies) and discussing the model’s potential for generalization beyond the initial demographic cohort.