Designing Energy-Efficient Wireless Language Learning Platforms for Remote Education
The increasing attention towards easily accessible remote education has created a demand for effective, wireless, and energy-efficient teaching aids, especially in the language learning field. This paper sets forth the design and implementation of language learning platforms that are wireless and energy efficient for remote teaching settings. The proposed system is designed around the objectives of energy efficiency and access to quality linguistic resources using low energy communication protocols, edge computing, and adaptive content delivery frameworks. The platform employs intelligent caching and machine learning user profiling systems to tailor languages instruction, augment user participation, lessen repetitive data transmission, and streamline instruction. Energy usage is also minimized through multimodal content delivery that allows for audio, visual, and interactive instruction to better fit the learner’s needs. More focus is placed on the context of low infrastructure and energy-constrained settings where power-efficient design is vital for sustainable access to education. The simulation and field test evaluations presented in the paper highlight improvements in terms of energy use while maintaining pedagogical effectiveness… as compared to mobile learning systems. This works expands the scope of green educational technologies by proposing a scalable and sustainable framework… responsive to the need for elbroch the digital divide and endorse inclusive education.
Authors
- Khikmat Saidov
- I.B. Sapaev (ORCID: https://orcid.org/0000-0002-2343-7454)
- Nigora Rasulova
- Mavluda Mavlonova
- Abdulla Khudaykulov
- Feruza Bakaeva
- Ollabergan Allaberganov
- Diyorbek Yoqubov
Publication Details
- Journal
- Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications
- Published
- 2025-06-30
- DOI
- https://doi.org/10.58346/jowua.2025.i2.018
- Citations
- 1
- Primary Topic
- Energy Harvesting in Wireless Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.73