Imagine you create an online course in English, but most of your potential students don't speak English well enough to follow along. This project built an AI translation system that can automatically translate everything in an online course — video subtitles, quizzes, assignments, discussion posts — into 11 different languages including German, Chinese, Russian, and Polish. It's like having a team of translators on standby, but powered by machine learning that gets smarter over time by learning from user feedback and quality checks.
Online education platforms lose massive potential markets because their courses are only available in English. Manual translation of all course materials — video subtitles, quizzes, assignments, discussion forums — into multiple languages is prohibitively expensive and slow. This creates a growth ceiling for any platform wanting to serve non-English-speaking learners across Europe and BRIC countries.
The project delivered a complete AI translation platform through 3 iterative prototypes, culminating in a final system that translates all MOOC content types (subtitles, tests, presentations, assignments, blog text) from English into 11 languages. The system includes automatic and human quality evaluation, crowdsourced feedback loops, sentiment analysis for user satisfaction, and was integrated as an open-source web service tested on the Iversity platform and VideoLectures.NET.
If you are an e-learning platform struggling to expand beyond English-speaking markets — this project developed a full translation system tested on the Iversity MOOC platform that handles all course content types (subtitles, quizzes, assignments, blog posts) across 11 languages. The final prototype integrated refined translation models with quality assurance through crowdsourced and expert evaluation.
If you are a corporate training provider needing to deploy the same courses across offices in 6+ countries — this project built machine translation models covering 11 European and BRIC languages with automatic quality evaluation. The system was designed to handle diverse text types from presentations to assessments, reducing the cost of manual translation for multilingual workforces.
If you are a video platform or localization company dealing with high volumes of lecture and educational content — this project developed specialized translation models for video subtitles and lecture content, tested on the VideoLectures.NET digital library. The system includes sentiment analysis on user feedback to continuously improve translation quality.
The EU contribution amount is not available in the dataset, so specific development costs cannot be stated. However, the system was built as an open-source web service (as noted in the System prototype version 2 deliverable), which suggests lower adoption costs compared to proprietary solutions. Integration costs would depend on your platform's architecture.
The system was designed for Massive Open Online Courses, which inherently involve large volumes of diverse content types. It was tested on two real platforms — Iversity and VideoLectures.NET — handling subtitles, assignments, tests, presentations, and blog text. The final prototype (version 3) integrated translation models for all 11 target languages.
The System prototype version 2 description explicitly mentions integration into an open-source web service, suggesting open-source licensing for at least parts of the system. For specific licensing terms and commercial use rights, you would need to contact the consortium led by Humboldt-Universität zu Berlin.
The system translates from English into 11 languages: German, Italian, Portuguese, Greek, Dutch, Czech, Bulgarian, Croatian, Polish, Russian, and Chinese. These were specifically chosen as languages that are hard to translate into and had weak machine translation support at the time of development.
The project used a multi-layered quality approach: automatic evaluation metrics, crowdsourced human evaluation with strict access controls, domain expert review, and end-user feedback. Sentiment analysis on user blog posts provided additional quality signals. All feedback was combined into a vector used to retrain and improve translation models.
The project ran for 3 years (February 2015 to January 2018) and is now closed. Three complete prototype iterations were delivered, with the final version incorporating all evaluation feedback and premium add-on services. The technology would need assessment for current state-of-the-art compatibility.
The consortium of 10 partners across 6 countries (Belgium, Germany, Greece, Ireland, Netherlands, UK) brings a solid mix of academic research and industry capability, with 6 universities providing NLP and machine translation expertise and 3 industry partners (all SMEs) ensuring commercial relevance. The 30% industry ratio is reasonable for a translation technology project. Led by Humboldt-Universität zu Berlin, a leading German research university, the project had direct access to real testing environments through partners connected to Iversity and VideoLectures.NET. The geographic spread across Western and Southern Europe aligns well with the multilingual mission of the project.
Humboldt-Universität zu Berlin, Germany — reach out to the NLP/Computational Linguistics department
Want to explore how TraMOOC's multilingual translation technology could work for your e-learning platform? SciTransfer can connect you with the research team and help evaluate fit for your specific language and content needs.
