Collaborative Learning Platform With Real-Time Chat and Course Search

Added Jun 2025 3 design docs

Online learning too often means watching videos alone; real education is collaborative, with discussion, shared materials, and instructors who respond in the moment. This project builds a learning management system designed around that collaboration rather than treating it as an add-on. The intern develops a Django and Python backend with PostgreSQL modeling courses, enrollments, materials, and conversations, and integrates Elasticsearch so students can instantly search across course catalogs and content. Real-time chat connects students and instructors inside each course, file uploads let both sides share materials, and role-based access separates students, instructors, and administrators. Authentication supports OAuth login alongside standard accounts, and protected routes keep course content restricted to enrolled users. The frontend is built with Next.js, React, and TypeScript, styled with Tailwind CSS for a responsive, modern learning experience. The project stretches the intern across real-time features, search infrastructure, and third-party authentication in one coherent product, demonstrating the ability to assemble multiple advanced subsystems into a scalable platform, the defining skill of senior fullstack work.

Related projects

Student Records and Course Workflow Backend for Institutions

Schools and universities run on administrative workflows, including enrollment, course management, grading, and notifications, and most institutions still stitch these together from spreadsheets and email. A robust workflow backend that automates records and communications is genuinely transformative infrastructure, and building one demands professional-grade engineering. The intern builds that backend in Java with Spring Boot. The system manages student records, course catalogs, and enrollment through JPA-backed entities in PostgreSQL, with robust validation guarding every input. Automated notifications for events like enrollment confirmations and grade postings run through async processing so the API stays responsive. Security is production-styled: JWT authentication and role-based authorization separate what administrators, faculty, and students can do. The build is managed with Maven and deployed with Docker, and an optional React frontend provides admin and faculty dashboards over the API. The project teaches layered backend architecture, asynchronous job design, and security implementation to a standard that mirrors enterprise Java work. The intern completes it with a deployable system and the vocabulary to discuss real workflow automation in interviews.

educationdockerjava+4

View project

Medium

Batch Grading Analytics Pipeline for Student Assignments

A university processing tens of thousands of assignment submissions each term needs more than spreadsheets to compute grades, detect anomalies, and report cohort statistics. This project recreates that challenge as a distributed data workflow that an intern can build and run end to end. Submission events are simulated with Kafka so assignments arrive as a realistic stream throughout the exercise, and raw submission data is stored durably in HDFS. Scheduled PySpark jobs then process the accumulated submissions in batches, computing grading summaries, score distributions, completion rates, and per-course statistics. Python scripts manage the surrounding workflow, handling ingestion, triggering batch runs, and exporting results as structured data files and reports for instructors and administrators. There is no web or mobile front end; the deliverables are the pipeline itself and the reports it produces, which keeps attention on data correctness and throughput. The intern comes away understanding the streaming-to-batch pattern used across industry big-data platforms: durable ingestion, distributed computation, and reproducible reporting, exercised on a domain where the meaning of every metric is easy to reason about.

educationtechnologybig-datahdfs+3

View project

Medium

Retrieval-Backed Course Material Generator for Banking Education

Trainers and educators in financial services spend enormous effort producing course notes, quizzes, and explanations for every topic they teach, and the material dates quickly as products and regulations change. A generator that assembles fresh, accurate learning content from a maintained resource library changes the economics of banking education. The intern builds this generator in python with a streamlit interface. Educational resources, including articles, definitions, and worked examples, are embedded and stored in a vector database. When a user enters a banking topic or uploads their own learning materials, a retrieval-augmented generation pipeline gathers the most relevant source content and generative AI composes tailored outputs: explanatory lessons, topic summaries, and quiz questions with answers. Because generation is grounded through RAG in the stored resources, the produced material reflects the curated corpus rather than the model's unverified memory, and users can regenerate or refine content interactively. The project teaches content pipeline design, grounded generation for educational quality, and quiz construction logic, giving the intern a strong demonstration of AI applied to learning-content operations in a regulated domain.

bankingeducationgenerative-aipython+4

View project