The global trend towards educational digitalization

exemplified by initiatives like "AI+Education

" often encounters infrastructural and policy constraints in real-world settings. A prevalent yet under-explored constraint is the restriction of student access to smart learning platforms to weekends

a policy common in various educational contexts due to concerns over screen time

digital equity

and holistic development. This limitation fundamentally challenges the real-time

interactive paradigms central to many technology-enhanced learning models. In response

this study proposes

implements

and critically examines a data-asynchronous teaching model. Operationalized through the cycle of "weekend learning → data generation → teacher interpretation → instructional guidance → assignment of new weekend tasks

" the model is guided by the core principle of "weekend data collection → weekday teaching application." It strategically repurposes the temporal delay from a hurdle into a diagnostic planning interval

transforming weekend-generated learning analytics into a robust evidence base for precise

data-informed instructional decisions during the subsequent school week

thereby facilitating differentiated and personalized pedagogy. Grounded in a senior secondary education context

this paper elaborates a detailed three-phase implementation pathway

moving beyond mere description to offer a dialectical analysis. This analysis critically appraises the model's scalability and its potential to empower teachers as pedagogical designers

while also rigorously engaging with inherent challenges such as data latency

dependency on weekend compliance

and the imperative to preserve essential human-centered

emotional interactions in learning. The findings contribute to the discourse on educational technology by demonstrating the feasibility and reflective practice of a pragmatic

teacher-led

and data-driven approach to enhancing instructional quality and equity within environments of constrained technological access.