<![CDATA[Purpose: This study critically examines the disparity between the pedagogical conceptualization of Deep Learning (DL) and on-the-group operational realities. Furthermore, it delineates a strategic framework to align policy mandates with the elementary school ecosystem in anticipation of the national rollout in the 2025/2026 academic year. Method: Employing a qualitative archival research design, this study utilizes comparative analysis to juxtapose policy documents (2025 Technical Guidelines) against empirical data (teacher surveys and 2022 PISA reports). Content analysis is leveraged to map the dissonance between the expected pedagogical roles of teachers and their actual administrative workloads. Findings: The findings elucidate significant cognitive distortion, revealing that 59.7% of teachers conflate DL with Artificial Intelligence (AI) technology rather than viewing it as a pedagogical approach. Primary impediments include onerous administrative burdens and infrastructural disparities. Pivotal strategies for successful adoption include practice-based simulation training and the transformation of school principals into instructional leaders. Practical implications: These results necessitate a paradigm shift from theoretical dissemination to practical workshops. Furthermore, the study advocates for the deregulation of administrative workloads as a sine qua non for effective implementation. Originally/value: The study's novelty is predicated on its function as a comprehensive “early warning system," scrutinizing ecosystem redliness prior to full-scale policy enactment. This distinguishes it from standard post-implementation evaluation, offering a protective rather than reactive analysis]]>
