Measurement and Validation Standards for Context-Rich Conversational AI Systems

Abstract

High-context conversational intelligence requires an AI system to not only interpret direct linguistic content but also maintain continuity of meaning across time, infer implicit intent, and adapt to subtle shifts in tone, social expectations, and situational framing. Existing evaluation methods often measure surface-level correctness and fluency while overlooking deeper discourse reasoning processes that enable effective, human-aligned dialogue. This study proposes a structured benchmarking framework that evaluates conversational models across multi-turn context retention, semantic continuity, adaptive inference, pacing modulation, and robustness under context distortion. Experimental analysis shows that conversational performance is strongly influenced by how representation memory and contextual inference are encoded rather than by model scale alone. Further, enterprise workflow integration tests highlight that sustained conversational coherence depends on coordination between inference layers and application-level session persistence mechanisms. The findings establish a foundation for benchmarking and developing conversational systems capable of reliably supporting high-context human–AI communication.