The device only connects to the internet when downloading content or uploading learning history. This enables digital learning even in environments where internet connectivity is difficult.
Using the existing features of Goocus, administrators can manage the progress of learning, as well as deliver updates and new content.
By reducing the data size of videos, images, and other content, we enable quick downloads to mobile devices and smooth operation during learning.
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By installing Edge devices equipped with Goocus functionality, Edge Computing* enables stable digital learning and learning management even in low-speed network environments.
By connecting Edge devices to the Internet, it is possible to aggregate information from multiple devices and manage learning more efficiently.
With the conventional Goocus features, administrators can manage the progress of learning, deliver updates, and distribute new content.
*Edge Computing is a network technology that performs data processing and analysis on Edge devices.
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To address challenges unique to developing regions—such as frequent power outages, unstable connectivity, and the need for mobile or temporary setups—we incorporated insights gained directly from on-the-ground operations into the product design.
The system also provides full UI support for local languages such as Swahili, Amharic, and Yoruba*, enabling ministries of education, teachers, and learners to use it immediately without language barriers.
*Major regional languages in Africa: Swahili (East Africa), Amharic (Northeast Africa), Yoruba (West Africa)
The system seamlessly unifies all essential educational components—from access points and gateway functions to the cloud platform, LMS, and administrative tools.
Integrated with the self-directed learning LMS GOOCUS, it delivers content management, learning analytics, and administrator features in a single package.
With a built-in SIM, the device operates independently even without wired, Wi-Fi, or LTE networks, enabling automatic cloud sync, content updates, and learning record uploads with no extra effort from administrators.
Students can download content through the mobile app and continue learning offline. The system runs on the notebook PC’s built-in battery, ensuring operation even during power outages, and it is also compatible with high-capacity mobile batteries.
Because it requires no fixed installation and is easy to carry, set up, and relocate, the solution can be deployed flexibly—from schools and villages to refugee camps, remote areas, and temporary environments such as short-term training or examinations.
*“Off-grid” refers to the ability to operate independently without relying on power or communication infrastructure.
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(Left: Edge server (notebook PC with SIM slot), Top: Access point, Right: Mobile battery)
Learning in Action
*360-degree video is a video shot with a 360-degree camera that can be freely viewed from all directions.
A virtual learning environment specialized for VR theaters. Since it does not require traditional VR glasses, it enables group education and experiences with communication similar to classrooms.
With Goocus VR, you can easily add text and quizzes to all VR videos, including 360-degree videos, without the need for specialized software or editing techniques.
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Goocus AI aims to develop "lightweight AI" by focusing on learning functions, resulting in a small data footprint and fast operation. Unlike traditional AI, it does not require massive datasets or communication during execution. This enables individualized, AI-driven learning on smartphones without the need for an internet connection.
Goocus AI generates recommended questions suitable for the individual learner or asks existing questions depending on whether the answer is correct or incorrect.By solving similar problems repeatedly, you can expect to overcome areas of weakness.
By learning the overall correctness tendencies for each question from all students, AI is expected to classify questions by difficulty level, enabling learners to understand their own abilities. Additionally, it supports more accurate adaptive learning based on individual correctness trends and trends in the correctness of question sets.