The growing adoption of generative artificial intelligence (GenAI) in organizational settings challenges the continued applicability of established knowledge management (KM) theories, particularly in explaining how tacit and explicit knowledge are created and transformed in AI-augmented environments. While Nonaka’s SECI model remains a foundational framework for conceptualizing knowledge conversion, it provides limited operational guidance for governing human-centered knowledge processes under increasing algorithmic mediation. This paper proposes a conceptual framework that extends the SECI model by integrating design thinking as an operational control layer for knowledge conversion in the generative AI era. By systematically mapping design thinking phases onto the SECI knowledge spiral, the framework specifies how empathy-driven inquiry, structured articulation, synthesis, and experiential learning regulate socialization, externalization, combination, and internalization processes. Generative AI is positioned as a mediating enabler that augments knowledge synthesis and exploration while preserving human agency, interpretive authority, and accountability. The proposed framework enhances the explanatory and operational clarity of the SECI model and provides a systems-oriented foundation for responsible and sustainable knowledge creation in AI-augmented organizational contexts.

Knowledge in Braille is ultimately necessary to maintain learning for the visually impaired. In the Philippines, class attendance has been showing low rates for visually impaired students caused by the shortages of teachers and the absence of the specialized tools intended for teaching them. A proposed solution in addressing this problem is the usage of computers for the automation in the process of the extraction of information in Braille which can facilitate teaching. In recent years, a considerable amount of effort and attention have been devoted to the development of this kind of technology however in languages other than Filipino Braille. Codes in Filipino Braille with its contractions, and even the Filipino language itself has unique features as compared with other languages. In this paper, a system is proposed which uses machine vision in recognizing Filipino Braille codes including one-cell and two-cell contractions. Synthetic Braille images undergo cascade object detection, image processing, extraction of HOG features to develop the three-stage multiclass SVM classifier. Experimental evaluation results reveal a good performance of Filipino Braille classification and translation to texts.