Spring Boot Online Teaching Management Platform: AI-Powered Learning and Assessment

Traditional educational models are increasingly strained by their inherent limitations. Physical classroom capacities and fixed schedules restrict accessibility and scalability. The scattering of digital learning materials across various platforms hinders unified access and management. Furthermore, the lack of dynamic, real-time interaction post-class leads to lower student engagement and slower feedback loops. Manual processes for attendance, grading, and analysis are not only time-consuming but also prone to errors. The burgeoning online education market, valued at 650 billion yuan in China with over 450 million users and a 18% annual growth rate, underscores the urgent need for digital solutions that address these pain points and enhance the overall learning experience.

The platform is designed with a rich set of features to empower both students and educators. For students, it offers intuitive course browsing with classification by subject and difficulty, multimedia content playback with adjustable speeds and note-taking capabilities, and personalized course and exercise recommendations based on their learning history and performance. Real-time interaction through live class features like bullet comments and Q&A sessions, along with post-class asynchronous support, are integral. Students can submit assignments online, take proctored exams with anti-cheating measures, and track their learning progress through detailed analytics. Teachers benefit from streamlined course management, including content upload and scheduling. They can conduct live lectures with interactive tools, manage and grade assignments efficiently, and monitor student performance. Administrators oversee user management, course approvals, and system-wide data analytics, ensuring smooth platform operation and strategic oversight.

Artificial Intelligence plays a pivotal role in elevating the platform's capabilities. Recommendation algorithms, including collaborative filtering (User-Based CF, Item-Based CF) and deep learning models like Wide & Deep, will provide highly personalized course and exercise suggestions. For assessment, an intelligent grading system will automate the evaluation of objective questions based on predefined rules and utilize Natural Language Processing (NLP) techniques, such as BERT models, for semantic analysis of subjective answers. Programming assignments will be assessed through secure sandbox environments. AI will also be crucial for anti-cheating measures in online exams, employing facial recognition (OpenCV, Dlib), screen monitoring, and tab-switching detection to ensure academic integrity. This AI integration aims to create a more adaptive, efficient, and secure learning environment.

The project is structured with a clear development roadmap. Initial phases involve topic selection, research, and data collection (Oct-Nov 2024), followed by proposal defense and argumentation (Dec 2024). The first draft is scheduled for completion by April 2025, with revisions and finalization by May 2025. Expected outcomes include a fully functional platform supporting over 5,000 concurrent users with an average response time under 500ms. Performance benchmarks aim for a throughput exceeding 800 TPS with an error rate below 0.1%. The intelligent grading system is projected to reduce subjective grading time from 5 minutes to 10 seconds per submission. Furthermore, the project aims to secure one software copyright and publish a core journal paper on AI-based automated grading algorithms. The final product will adhere to the specified technical stack and user interface.