Algorithmic Logic and Scene Reconstruction: The Three-Tier Structure and Implementation Framework of Artificial Intelligence Applications in Educational Publishing

Amid the accelerating penetration of artificial intelligence into the educational domain, algorithmic logic is powerfully reshaping the operational mechanisms and interactional boundaries of educational publishing, reshaping the underlying paradigm of knowledge services and reconfiguring the rules governing their constitutive elements. Prevailing practice, however, tends toward a technological-determinist trap, treating AI adoption as a context-free, uniform process of efficiency gain. Against this backdrop, this study introduces the perspective of scene theory (Scene Theory) to move beyond a narrow functional evaluation of technology and conceptualize educational publishing as a dynamic field in which technological logic, content provision, and pedagogical situations are mutually nested. Methodologically, the study proceeds through conceptual clarification and structured case analysis: it first delimits the analytical construct of the educational-publishing "scene, " decomposing it into four interacting dimensions—the material–media environment, temporal rhythm and task structure, subject roles and power relations, and value norms and evaluation systems—and then uses this framework to analyze the process by which AI intervenes in educational publishing. Drawing on a range of illustrative cases from Chinese and international publishers, the analysis deconstructs AI applications into three progressive layers. At the embedding layer, AI operates chiefly as an auxiliary tool that enters established workflows with low disturbance, translating general-purpose technology into situated capability and restructuring production processes through rule-based governance; as evidenced by the conversion of external rigid constraints into internal compliance routines, situated adaptation of platforms, and the encoding of editors' tacit expertise into explicit system rules. At the service layer, AI shifts from back-end production to front-line teaching, acting as a service medium that links publishers, teachers, and students, thereby driving a transition from static product delivery to dynamic, process-oriented knowledge provision, reconfiguring organizational structures and the interaction logic between publishers and the teaching site. At the integration layer, technology turns toward building platform infrastructure and organizing ecological coordination, extending the publisher's role to that of rule-maker, resource organizer, and ecosystem coordinator, and advancing data-driven content co-creation and multi-stakeholder governance. The study argues that the effectiveness of AI-based restructuring depends not on platform scale or computational power per se but on the degree of fit between AI and the deep-seated structural elements of educational scenes. Accordingly, it proposes three coordinated implementation paths: media-environment adaptation grounded in prior rule-setting and system compatibility, which compresses operational friction and embeds external regulation as automated underlying processes; dynamic feedback loops that run through the temporal sequence of lesson preparation, teaching, assignment, and assessment, establishing fine-grained mappings between content and learning behavior while preserving teachers' control over pedagogical rhythm; and platform-based collaborative governance that integrates the interests of multiple stakeholders and educational value norms through codified rights-and-responsibilities lists, institutional constraints on algorithmic boundaries, and periodic rule review. The study concludes that only through institutional compatibility and ecological integration can artificial intelligence drive educational publishing toward its deeper transformation into process-oriented knowledge services, while publishers must concurrently assume the role of value steward and ecological gatekeeper.