Academic Article Finder and Summarizer for Coursework

For most students, the hardest part of an assignment is not writing it but assembling the reading: locating articles that actually address the essay question and extracting their arguments without losing an afternoon per paper. Library databases return abstracts written for specialists, and students without research training struggle to judge relevance quickly, so weak sources get cited and strong ones get missed.

The intern builds a Streamlit application in Python that guides a student through this process. The student describes their assignment topic, and the app uses LangChain to structure a research workflow: generating focused search queries, evaluating candidate articles for relevance to the stated question, and producing summaries through OpenAI models that highlight each article's argument, evidence, and usefulness for the assignment. The intern implements the workflow chains, a reading-list view where selected articles accumulate with their summaries, and an export feature that produces a formatted starting bibliography.

The project is an approachable introduction to chained LLM workflows: the intern learns to compose LangChain steps into a purposeful sequence, design prompts that evaluate rather than merely generate, and ship a Streamlit interface that turns a research method into a tool any classmate could pick up and use.