The purpose of this study is to enhance AI model explainability and value alignment in resource-limited environments by transforming the traditionally opaque black-box models into transparent, interpretable systems. By embedding DIKWP semantic reasoning and System 2 cognitive control into distributed learning frameworks, this study seeks to monitor and guide inference paths in real-time. This ensures alignment with user purposes and security expectations without incurring high resource costs, offering a viable solution for privacy-preserving, trustworthy AI deployment on edge devices such as smartphones, wearables and home IoT systems.
This study proposes a DIKWP-based white-box semantic distributed learning framework tailored for resource-constrained devices. It integrates dual-process cognitive theory (System 1/System 2) and embeds semantic probes into models to monitor DIKWP transformations—Data, Information, Knowledge, Wisdom, Purpose—during inference. A DIKWP×DIKWP transformation matrix quantifies semantic transitions, enabling transparent reasoning path tracking. Lightweight probe mechanisms allow model introspection with minimal computational overhead. The framework is evaluated on reasoning-intensive data sets via metrics such as semantic unit coverage, cognitive path entropy and reasoning step frequency, validating its effectiveness in enhancing explainability and safety under federated learning and bandwidth-constrained environments.
Experimental results across CMMLU, Math23K and MMLU data sets show that the DIKWP-WISE framework significantly improves semantic reasoning depth, transformation coverage and cognitive entropy compared to traditional models. Models using DIKWP probes exhibit higher ratios of System 2 (deliberative) reasoning, fewer inappropriate responses and better purpose alignment. Notably, even under resource constraints, the semantic probes maintain performance without adding substantial computational load. Moreover, the framework enables semantic-level federated reasoning, contributing to both model safety and explainability, particularly in knowledge-intensive or user-critical tasks such as education, health care and intelligent interaction.
This paper pioneers a semantic white-box reasoning framework combining the DIKWP model with cognitive psychology to achieve real-time introspection of AI models in edge environments. Unlike traditional post-hoc explainability techniques, the DIKWP-WISE architecture embeds transparent reasoning directly into the model’s operation using semantic probes. This approach uniquely aligns semantic understanding with purpose-driven inference and provides a scalable, architecture-agnostic mechanism for secure, explainable AI on low-power devices. It bridges the gap between symbolic and sub-symbolic reasoning while offering practical contributions to secure federated learning and human-aligned decision-making systems.
