The domain of education has taken great leaps by capitalizing on technology and the utilization of modern devices. Nowadays, the established term "one size fits all" has begun to fade. The research focuses on personalized solutions to provide a specially designed environment on the needs and requirements of the learner. The adaptive platforms usually use Learning Styles to offer a more effective learning experience. This review analyzes the learner model, adaptation module, and domain module, originating from the study of 42 papers published from 2015 to 2020. As more modern techniques for adaptation get incorporated into e-learning systems, such techniques must be compliant with educational theories. This review aims to present the theoretical and technological background of Adaptive E-learning Systems while emphasizing the importance and efficiency of the utilization of Learning Styles in the adaptive learning process. This literature review is designated for the researchers in this field and the future creators and developers of adaptive platforms.

Extended reality (XR) has emerged as a transformative technology, offering innovative ways to visualize and interact with digital content. For educators, XR constitutes a valuable tool that advances pedagogical experience and improves teaching quality and clarity. While the literature highlights case studies and general guidelines for XR content development, there is limited focus on interaction techniques based on a comparative methodology within educational XR games. This study evaluates different interaction techniques from developers and users perspectives to identify strengths and limitations, providing useful insights to guide future developments in the field. Performed analysis determines the context in which each technique is most effective, how different techniques can be combined, and how integration can be improved for optimal impact. Additionally, methods for transitioning from traditional interaction techniques to modern XR approaches utilizing 3D space and interaction requirements are proposed. A theoretical framework for integrating, configuring, and blending interaction techniques in XR environments tailored for educational purposes is introduced to assist developers and educators in selecting and combining techniques to maximize their effectiveness in different educational contexts and challenges. By addressing these critical aspects, this paper contributes to advancing the understanding and design of XR interaction strategies, ultimately fostering better learning experiences and leading to improved educational outcomes.

Adaptive recommendation mechanisms are widely used to personalise digital learning environments; however, many existing approaches prioritise algorithmic optimisation while providing limited insight into how recommendation behaviour aligns with pedagogically structured instructional artefacts, such as worksheets. To address this gap, this paper proposes a hybrid recommendation approach for adaptive worksheet generation that integrates content-based and collaborative filtering with explicit pedagogical constraints derived from Bloom’s Revised Taxonomy. The system ranks and selects learning and evaluation objects across cognitive levels by combining learner profiles, behavioural signals, and similarity-based information within a unified scoring framework. A simulation-based evaluation was conducted to examine the internal behaviour, stability, and instructional alignment of the recommendation engine under controlled conditions, using Bloom-aligned worksheets and synthetic learner profiles. The analysis focuses on expected–actual alignment and adaptive variation across cognitive levels rather than learning outcomes. Results indicate strong alignment with the intended instructional structure at lower cognitive levels, while bounded and interpretable adaptive variation emerges at higher levels. Evaluation object recommendations showed high agreement with the instructional design, exceeding 95% across simulated conditions. Overall, the study demonstrates how hybrid recommendation mechanisms can support adaptive content selection in pedagogically structured learning scenarios, offering a transparent and robust foundation for information-driven educational systems.