Abstract:

The rapid expansion of digital education has increased the need for learning environments that can move beyond one-size-fits-all content delivery and support immediate adoption by tutors across diverse educational contexts. This paper presents the proposed architecture and platform design of a tutor-assisted adaptive learning platform for the personalized delivery of digital learning experiences. The platform is intended for use across formal and non-formal education settings and is designed to support both supervised and less supervised learning scenarios.

The proposed system addresses a central limitation of many existing digital learning environments: they either adapt learning paths to learner performance or allow configurable delivery options but rarely integrate both capabilities in a unified and operational architecture. Our approach combines adaptive learning, which adjusts the sequence and level of content according to learner progress and demonstrated understanding, with adaptable learning, which supports personalization according to preferred modes of content engagement and instructional configuration. This dual orientation is a defining feature of the proposed platform and forms the basis of its pedagogical and technical design.

At the architectural level, the platform is conceived as a cloud-based, web-based, multi-tenant software environment that enables scalable deployment and flexible use by different educational organizations. Its core design includes modules for learner data management, learner profiling, content and assessment management, personalization and recommendation, adaptive decision support, and delivery of individualized learning experiences. The architecture is designed to collect and process learner interaction data, assessment evidence, prior learning information, and usage patterns in order to generate dynamic learner profiles and support personalized learning pathways.

Artificial intelligence (AI) is embedded across the platform as an enabling mechanism for continuous personalization. It supports the estimation of learner knowledge levels, the identification of learning needs, the recommendation of suitable content, and the selection of delivery forms that better match learner characteristics and performance patterns. At the same time, the tutor-assisted orientation of the platform is intended to lower adoption barriers and provide practical guidance to tutors, allowing personalized learning experiences to be introduced in real educational practice without requiring radical instructional redesign.

The contribution of this paper lies in the presentation of a coherent proposed architecture that brings together platform design, personalization logic, and tutor-oriented operational use within a single framework. The paper aims to contribute a practical and extensible architectural foundation for the development of next-generation digital learning systems that are scalable, pedagogically meaningful, and suitable for broad educational adoption.

Keywords:

Adaptive learning systems, e-learning, AI-driven learning systems.