Recent intelligent automated scoring systems learn nonlinear patterns from multimodal biosignals and produce probabilistic decision outputs. In real operational settings, however, these probabilistic outputs often require additional interpretation by users. This can increase cognitive burden and reduce decision consistency and responsiveness. This study identifies the interpretive gap between AI outputs and human cognitive structures as a core design problem. To address this issue, we propose a human-centered data–interface mapping framework. The framework introduces an intermediate interpretation layer that converts continuous probability values into meaning states aligned with users’ judgment models. These meaning states are then systematically mapped to intuitive multisensory interface cues, such as visual and auditory signals. Through this transformation, internal computational representations are reshaped into interpretable units for users. As a result, the judgment workflow shifts from number-based interpretation to perception-based understanding. We implement the proposed framework within an intelligent automated scoring system and define the transformation process among probabilistic outputs, meaning states, and sensory cues, along with the corresponding interface response logic. The framework is evaluated using a comparative task-analysis matrix against conventional scoring procedures, scenario-based walkthroughs, and consistency checks of interface responses. The contribution of this study lies not in improving model accuracy, but in formally defining and implementing an architectural mechanism that enables AI outputs to become structured human judgment experiences. This approach provides both theoretical and practical foundations for designing human-centered intelligent scoring systems in high-risk decision-support contexts.