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Research on human memory and retention has revived interest in systems that opti‐
mize review schedules to maximize learning while minimizing study time. This paper surveys
the current state of intelligent revision frameworks and outlines key challenges for the next
generation of tools. As a compact case study, Gutek, an open‐source, Java/Spring frame‐
work engineered for low‐code extensibility is examined. It leverages dependency injection,
reflection, and Spring Data JPA to automate component discovery and data persistence, en‐
abling rapid integration of new revision algorithms, card types, and statistical charts with
minimal coding effort. Additionally, Gutek supports bidirectional revision (regular and reverse
modes) and already implements representative spaced‐repetition strategies, while maintaining
comparatively low code complexity. Based on this landscape analysis, a concrete roadmap
is articulated. First, a fully featured API is proposed to support multi‐device deployments,
i.e., web for desktops, native extensions for mobile, and the existing desktop app, ensuring
synchronization, offline‐first operation, and privacy safeguards. Second, future work directions
are outlined, focused on foreign‐language learning to calibrate optimal scheduling parameters
and to train a deep learning model that predicts revisions under real‐world constraints. Third,
steps toward a deployable framework for public institutions, emphasizing maintainability,
accessibility, localization, and robust governance are discussed. Finally, cross‐cutting challenges
are identified: reproducible benchmarks beyond accuracy (e.g., latency, energy, cognitive load),
explainability of scheduling decisions, handling drift, and standards for interoperable decks,
logs, and models. The goal is to bridge systems engineering with learning science to deliver
customizable, trustworthy, and scalable intelligent revision infrastructure.
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