PRISM

Learning by doing now shapes how engineering is taught, letting pupils mix classroom theory with building real systems and testing them [1, 14]. Across the globe, colleges require seniors to complete projects before graduating, collecting stacks of new proposals every year. Yet this flood of work reveals a flaw schools haven’t fixed: without a central online library plus tools that check uniqueness automatically, similar or repeated ideas slip through again and again. Teachers then face the chore of spotting duplicates themselves, one by one. When project records sit scattered across paper folders, messy network drives, or locked-in learning platforms, pulling them back later feels like a guessing game. Retrieving old work for comparison often takes more effort than it should - time adds up quickly when files lack structure. Growing student numbers mean more problem-based assignments flood in each term. Reviewing every one by hand? That path wears thin fast. Systems built on routine checks start creaking under the load. Earlier studies on finding information [2], handling knowledge [3], and spotting copied text using natural language processing [7, 9] have shown that machines can judge how similar written content is - useful when checking if students follow academic rules. Still, tools like Turnitin or iThenticate focus mostly on catching word-for-word copying in finished research papers; they fall short when it comes to detecting reused ideas in early drafts of projects. In another case, broad academic storage systems - say, university-run DSpace setups - save documents but lack built-in checks for matching concepts or fine-grained permissions shaped around the roles of learners, instructors, and staff common in project-based learning settings. Something different shows up now - say hello to PRISM, which stands for Project Repository with Intelligent Similarity Matching, made to fix current flaws by combining clever file storage with fast idea reviews driven by language understanding tech. Not simply saving documents anymore, it judges how much new entries resemble earlier work using TF-IDF plus cosine similarity, shaped by a clear sequence of data cleanup steps. Results come back as instant verdicts: Accept, Change, or Decline - even if instructors are free to revise those choices afterward. Underneath, the whole system moves on a full MVC structure, giving distinct screens designed for each of three key user roles.

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