Review Collection System
Project description
This project aims to create a review collection system that follows GDPR rules and uses AI for moderation. The goal is to help prospective students make better decisions about university master’s programs by providing honest and useful reviews. The system will ensure that all reviews are genuine and relevant by using AI to filter out inappropriate language. This will give prospective students reliable insights into different programs, helping them choose the right one. Overall, the project seeks to make the review process more transparent and helpful for future students.
Context
In the context of higher education, choosing the right master’s program is a crucial decision for prospective students. However, finding authentic and useful information about different programs can be challenging. Many students rely on online reviews to gain insights, but these reviews can be unreliable or biased. This project addresses this issue by developing a review collection system that complies with GDPR and uses AI for moderation. By ensuring that reviews are genuine and relevant, the system aims to provide prospective students with trustworthy information, thereby enhancing their decision-making process. This project operates within the domain of educational technology, focusing on improving transparency and reliability in student reviews to support better academic choices.
Results
The most important outcomes of our project include the development of a GDPR-compliant review collection system that uses AI for moderation, significantly enhancing the decision-making process for prospective students.
Products and Insights:
1. GDPR Compliance:
Implemented a privacy-by-design approach, ensuring that all personal data is handled according to GDPR standards. This includes obtaining explicit consent from users, transparent data processing practices, and robust security measures like encryption. Users have control over their personal data, with options to anonymize reviews or opt-out of sharing certain information publicly.
2. AI-Powered Moderation:
Deployed AI algorithms to filter and moderate user reviews, ensuring the content is relevant and authentic. The AI system assists in quickly identifying and flagging inappropriate content, with human oversight to ensure accuracy and context sensitivity. This hybrid approach ensures that reviews are moderated effectively while minimizing biases and errors.
3. Enhanced User Experience:
Designed a user-friendly interface that allows students to easily submit, view, and filter reviews. Features include a structured review format, category filters (e.g., location, tuition, teachers), and aggregated ratings to provide comprehensive insights. The system supports multiple languages, catering to a diverse user base, and ensures accessibility for users with disabilities by adhering to WCAG standards.
Technical Validation:
The system’s architecture is built using a technology stack, including Azure for cloud services, PostgreSQL for the database, and a combination of ASP.NET Core and Vue.js for the backend and frontend, respectively. The implementation includes automated testing and continuous integration processes to ensure reliability and performance.
Scalability and Security:
Designed to handle a high volume of users, the system is scalable and responsive, accommodating the needs of 55 million annual users without performance degradation. Security measures protect against common threats, ensuring the safety and integrity of user data.